Ecommerce MGMT, Author at Ecommerce Management Education

May 20 2026

Mt. Stupid Has A Pricing Page via @sejournal, @pedrodias

“There is now ample evidence, collected over the last few years, that AI systems are unpredictable and difficult to control.” That’s Dario Amodei in January, writing about the technology his company sells.

Compare with what’s on your LinkedIn timeline this week. Here’s the script: Schema markup ensures AI engines parse your content. The first sentence of every section must be the answer. Optimize for chunk-level retrieval. There’s a 13% citation lift available if you do X, a 2.8x conversion improvement if you do Y.

It’s one of the cleanest patterns going right now, and the industry has elected not to notice. The people closest to these systems are increasingly cautious about claims of control. The people furthest from it are increasingly certain they know how it works … they’ve cracked it. That gradient runs the wrong way.

What The People Who Built It Actually Say

Anthropic published its main interpretability research post in May 2024. It opens:

“We mostly treat AI models as a black box: something goes in and a response comes out, and it’s not clear why the model gave that particular response instead of another.”

Anthropic, writing about its own model, two years ago.

Things haven’t gotten more confident since. Neel Nanda, who runs Google DeepMind’s mechanistic interpretability team, gave an interview to 80,000 Hours in September 2025 in which the headline finding was that the most ambitious version of mech interp is probably dead. He doesn’t see a realistic world where the discipline delivers “the kind of robust guarantees that some people want from interpretability.” Worth re-reading.

The person whose job is to read AI minds is publicly conceding that the project, as originally conceived, won’t get there.

At NeurIPS 2024, Ilya Sutskever, co-founder of Safe Superintelligence and formerly chief scientist at OpenAI, accepted his Test of Time award and used the platform to say something the room wasn’t expecting from him:

“The more it reasons, the more unpredictable it becomes.”

Sutskever’s career is essentially the scaling hypothesis with a face on it. Hearing him say the next phase produces less predictable outputs is itself an admission.

Now scroll back to your timeline. The gradient is Dunning-Kruger redrawn at an industry scale: Mt. Stupid with a pricing page, and the valley of calibration where the actual work happens.

What The People Selling It Actually Say

A practitioner posts a four-pillar framework for “Technical GEO.” A consultant guarantees inclusion in AI Overviews. An agency markets a 13% lift in citation likelihood, derived from data the agency itself produced about the agency’s own prescriptions. A widely shared post promises that maintaining a 300-character paragraph limit dictates how a vector database chunks your content. A vendor claims a 78% “share of model.” A senior figure in your inbox describes a 2.8x improvement in conversion from being cited in SGE.

The vocabulary is deterministic: “ensures,” “guarantees,” “dictates,” percentages precise to the decimal, frameworks confidently named. None of it sounds anything like the language the people who built these systems use when describing how the systems behave.

This is the part I keep getting stuck on. The consultants are confident about the tactics they’ve measured against themselves. Run the same playbook on a few clients, watch some metric move, call it evidence. No control groups, no pre-registered hypotheses, no measurement of what the tactic is actually claimed to change. That’s the bar a real test has to clear; everything else has been confirmation in costume. The problem is the confidence level, which is wrong by an order of magnitude regardless of whether the underlying tactic does anything. The same model that Anthropic publicly says it cannot fully account for is being optimized against by people who confidently claim to know exactly what they’re doing.

Either Anthropic has been suspiciously modest in public, or somebody else is suspiciously certain.

When Somebody Tests

On Monday, last week, Ahrefs published a study by Louise Linehan and Xibeijia Guan with a title that should ideally be impossible: We Tracked 1,885 Pages Adding Schema. AI Citations Barely Moved.

The methodology is the kind of work you would expect to be standard, if the discipline cared about standards. 1,885 pages that added JSON-LD schema between August 2025 and March 2026. 4,000 matched control pages. Citation changes measured 30 days before and 30 days after the schema was added, across Google AI Overviews, Google AI Mode, and ChatGPT. Difference-in-differences on the matched groups.

The finding: No meaningful uplift in citations on any platform. AI Overviews actually showed a small but statistically significant decline. The report notes the odds of a gap that large being chance are roughly 1 in 2,500. The schema-makes-LLMs-understand-your-content thesis, tested at scale against a controlled baseline, did not survive the test.

This is the empirical confirmation of the technical case I made a week ago in The Whole Point Was the Mess: that LLMs read unstructured language, and that schema-and-chunking prescriptions are reasoning about an architecture that doesn’t exist. From first principles, two weeks ago. From controlled measurement, last Monday.

It is worth sitting with that. The dominant prescriptive category in the entire GEO playbook has been empirically falsified under controlled conditions, by a vendor with a substantial audience, in the open. And the frameworks keep selling.

Then Google Itself Answered

On May 15, 2026, Google published official documentation on optimizing for generative AI features in search. The page mythbusts the GEO prescriptions in writing: llms.txt files aren’t needed; chunking content isn’t required; rewriting content for AI systems isn’t necessary; special schema markup isn’t required; pursuing inauthentic mentions doesn’t help. The framing is unusually direct for a Google developer page:

“Many suggested ‘hacks’ aren’t effective or supported by how Google Search actually works.”

Google names Answer Engine Optimization and Generative Engine Optimization by their full terms and rejects the playbook outright.

That is the search engine the consultants claim to be optimizing for, telling its own developer audience that the optimizations don’t work. From first principles, two weeks ago. From controlled measurement, last Monday. From Google itself, last Friday. Three independent sources of the same answer, all within a fortnight. All ignored by the people selling the opposite.

The Cost Of Asking

This is where the diagnosis stops being polite.

Confident claims compound on these platforms in a way that skeptical corrections don’t. The difference is in who pays. Posting a confident claim costs you nothing. It gets engagement, builds an audience, generates inbound, makes the slide deck look forward-looking. If it turns out to be wrong, nothing happens. By the time anyone notices, everyone’s moved on to the next acronym.

Posting the correction costs you. It picks a fight. It marks you as a contrarian, or worse, as somebody who doesn’t get it. On LinkedIn, where most of this happens, it works against your professional brand. The algorithm will not reward it. The original poster owns the comment section and can ignore your methodology question while engaging with the congratulatory replies. Your reply lives in a collapsed thread.

There’s a specific move worth naming here. Ask a GEO consultant to explain, in plain terms, what their methodology actually does, what mechanism it acts on, what would count as evidence, what would falsify it. The response escalates into jargon. “Vector-space alignment.” “T1 query optimisation.” “Chunk-level semantic retrieval.” Real terms from machine-learning research, glued into combinations that sound rigorous and resist plain-language verification. The pattern works because it can. Asking “what does that actually mean” looks naive, and observers without the specific technical knowledge can’t tell which combinations are real and which are improvised on the spot.

Read the comments on any high-engagement GEO post. Fifteen replies in, 12 are agreements or “here’s another skill to add to your list.” Two or three offer diplomatically-framed skepticisms: “I would love to see more data,” or “the list is right, but…” The author engages substantively with the philosophical objection because pushing back against “this is too technical” is easy. The methodological objection, that the prescribed skills produce confident speculation without a measurement layer underneath, gets the politest burial.

What this adds up to is gaslighting at industry scale. The people reading the technology correctly get positioned as the ones who haven’t caught up; the prescriptions that controlled tests just falsified get sold as forward-looking. GEO has worked out how to make calibration look like the deficiency.

A recent X experiment captured the dynamic outside SEO. Someone posted a Monet painting and claimed it was AI-generated, asking the replies to explain its inferiority to a real Monet. Hundreds responded, confidently cataloging the “AI tells.” Flat brushwork, soulless composition, no cohesion, no soul. They were analyzing a Monet. The frame determined what they saw.

The original post, where a lot of the initial replies have now been deleted.

It’s the same trick. Vocabulary substitutes for substance; framing activates confirmation bias before any examination begins; the performance of analysis becomes what’s purchased rather than the analysis itself; “this is X” arrives before anyone checks whether it is. Once the frame is set, the analysis follows.

So the people most equipped to push back, the practitioners who’ve actually tried to test things, the technical SEOs who know what schema does and doesn’t do, the ones who can spot a fabricated lift number from across the room, stay quiet.

The result, on the timelines the C-suite reads, is a one-sided market.

The cost falls on the people who buy the claim. Clients pay for schema audits the Ahrefs study just falsified. Junior practitioners build careers on methodologies that won’t survive a controlled test. And the discipline burns credibility it will need later, when traditional search displaces further, and SEOs are expected to sit in rooms with engineering teams who’ve just spent two years watching the field confidently mis-call the technology.

Knowledge advances by trying to disprove your hypothesis, not confirm it. GEO does the opposite, runs studies designed to validate what it’s already selling. If the professionals claiming this expertise won’t even try to falsify themselves, who do we expect to believe us?

The Absence Is The Data

Strip the discourse, and what remains is the absence.

A serious technical field watches a controlled test contradict its dominant prescriptions, and the prescriptions keep selling. At that point, asking whether the prescriptions are wrong stops being the interesting question. That has been answered. The harder question is what’s wrong with a field that watches and doesn’t correct.

Same with the gradient. When the people who built the systems hedge and the people optimizing for those systems guarantee, asking who’s right stops being interesting. The researches and builders are right. Nobody who has worked on inference attribution thinks otherwise. The harder question is why the field lets the guarantees travel unchallenged.

The honest answer is that the incentives don’t pull toward correction. Confidence sells in ways caution can’t. The reportable framework wins the budget; the sensible assessment loses. And hedged language doesn’t fit on a pricing page where a guarantee fits perfectly.

None of this needs villains. The market for attention rewards confidence over calibration, every time.

You can keep watching the gradient run the wrong way. Or you can read what it actually is: an industry standing on Mt. Stupid, charging for the view.

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This post was originally published on The Inference.

Featured Image: Roman Samborskyi/Shutterstock

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May 20 2026

Google’s llms.txt Guidance Depends On Which Product You Ask via @sejournal, @MattGSouthern

Google’s Search and Chrome documentation now point in different directions on llms.txt, depending on whether the goal is Search visibility or agentic browser readiness.

Google Search recently published a new optimization guide that lists llms.txt among the tactics you don’t need for generative AI features. The guide groups it with content chunking, AI-specific rewriting, and special schema.

Days earlier, Google’s Lighthouse tool shipped version 13.3, which added a new Agentic Browsing category. The update includes an llms.txt audit that checks whether a site provides the file and flags server errors when retrieving it.

The Lighthouse documentation describes llms.txt as a way to provide “a machine-readable summary of a website’s content, specifically designed for LLMs and AI agents.” It adds that without the file, “agents may spend more time crawling the site to understand its high-level structure and primary content.”

What Google Search Has Said

Google’s Search team has maintained for over a year that llms.txt is not a Google initiative or something Google plans to adopt.

John Mueller compared llms.txt to the keywords meta tag, noting no AI services used it and bots didn’t request the file. He called building separate Markdown pages for bots “a stupid idea.”

At Search Central Live Deep Dive Asia Pacific, Gary Illyes and Amir Taboul confirmed Google was not pursuing llms.txt.

Google’s optimization guide explicitly states llms.txt should be skipped, providing the most recent direct statement from the Search team.

What Chrome’s Lighthouse Now Does

Lighthouse 13.3 ships with the Agentic Browsing category by default, checking WebMCP integration, agent accessibility, layout stability, and llms.txt.

The llms.txt audit only marks sites as “Not Applicable” if they return a 404; errors flag the audit. The Lighthouse docs describe llms.txt as an “emerging convention” at llmstxt.org, advising site owners to create and place it in their root directory.

This category is separate from SEO audits and indicates that llms.txt helps browser-based agents understand site structure, not improve search rankings or AI citations.

Google Has Been Here Before

Google’s internal teams have sent mixed signals on llms.txt before.

In December, Lidia Infante spotted an llms.txt file on Google’s Search Central developer documentation. Mueller responded on Bluesky with “hmmn :-/” and didn’t clarify further.

Dave Smart noted that the file appeared on multiple Google developer properties, including developer.chrome.com and web.dev. The pattern suggested an internal CMS platform update that automatically deploys llms.txt files, not a Search team decision.

The Search Central file was removed within hours, but files on other Google properties remained.

Why This Matters

Google’s answer on llms.txt varies by use case.

For Google Search, llms.txt isn’t needed for AI Overviews, AI Mode, or other generative AI Search features.

For browser-based agents, Lighthouse considers llms.txt optional in an experimental machine interaction category.

Guidance is split between different Google developer sites, which can lead to conflicting instructions when comparing Lighthouse or its llms.txt documentation with Google’s Search docs.

Looking Ahead

Google hasn’t commented on the documentation gap between the two product teams.

For many sites, creating a basic llms.txt file is simple, but maintaining it is questionable, given that Google Search states it’s unnecessary for AI Search visibility.

Featured Image: Stock-Asso/Shutterstock

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May 20 2026

More Organic Search Traffic, More Ad Revenue: 4 Publishing Workflow Fixes That Bring Both

This post was sponsored by WP Engine. The opinions expressed in this article are the sponsor’s own.

Why are we missing the SERP window on breaking stories we should be winning?
How are smaller outlets ranking faster than us on the same news?
Why is our ad stack tanking Core Web Vitals on our highest-traffic pages?

In most large newsrooms, the answer traces back to the same culprit: a fragile, patchwork legacy CMS held together with ad-hoc plugins. For SEO and growth teams, that’s a direct hit to organic search traffic and ad revenue.
Below are four publishing workflow fixes that move both metrics in the same direction.

The 4 Publishing Pillars That Improve SEO & Monetization

To stop paying this tax, media organizations are moving away from treating their workflows as a collection of disparate parts. Instead, they are adopting a unified system that eliminates the friction between engineering, editorial, and growth.

A modern publishing standard addresses these marketing hurdles through four key operational pillars:

Pillar 1: Automated Governance (Built-In SEO & Tracking Integrity)

Marketing integrity relies on consistency.

In a fragmented system, SEO metadata, tracking pixels, and brand standards are often managed manually, leading to human error.

A unified approach embeds governance directly into the workflow.

By using automated checklists, organizations ensure that no article goes live until it meets defined standards, protecting the brand and ensuring every piece of content is optimized for discovery from the moment of publication.

Pillar 2: Fearless Iteration (Continuous SEO & CRO Optimization Without Risk)

High-traffic articles are a marketer’s most valuable asset. However, in a legacy stack, updating a live story to include, for instance, a Call-to-Action (CTA), is often a high-risk maneuver that could break site layouts.

A modern unified approach allows for “staged” edits, enabling teams to draft and review iterations on live content without forcing those changes live immediately. This allows for a continuous improvement cycle that protects the user experience and site uptime.

Pillar 3: Cross-Functional Collaboration (Reducing Workflow Bottlenecks Between Editorial, SEO & Engineering)

Any type of technology disruption requires a team to collaborate in real-time. The “Sticky-taped” approach often forces teams to work in separate tools, creating bottlenecks.

A modern unified standard utilizes collaborative editing, separating editorial functions into distinct areas for text, media, and metadata. This allows an SEO specialist or a growth marketer to optimize a story simultaneously with the journalist, ensuring the content is “market-ready” the instant it’s finished.

Pillar 4: Native Breaking News Capabilities (Capturing Real-Time Search Demand)

Late-breaking or real-time events, such as global geopolitical shifts or live sports, require in-the-moment storytelling to keep audiences informed, engaged, and on-site. Traditionally, “Live Blogs” relied on clunky third-party embeds that fragmented user data and slowed page loads.

A unified standard treats breaking news as a native capability, enabling rapid-fire updates that keep the audience glued to the brand’s own domain, maximizing ad impressions and subscription opportunities.

If those are things you’ve explored changing, it may be time to examine your own Fragmentation Tax, and why a new publishing standard is required to reclaim growth.

Stop Paying The Fragmentation Tax: How A Siloed CMS, Disconnected Data & Tech Debt Are Costing You Growth

The Fragmentation Tax is the hidden cost of operational inefficiency. It drains budgets, burns out teams, and stunts the ability to scale. For digital marketing and growth leads, this tax is paid in three distinct “currencies”:

1. Siloed Data & Strategic Blindness.

When your ad server, subscriber database, and content tools exist as siloed work streams, you lose the ability to see the full picture of the reader’s journey.

Without integrated attribution, marketers are forced to make strategic pivots based on vanity metrics like generic pageviews rather than true business intelligence, such as conversion funnels or long-term reader retention.

2. The Editorial Velocity Gap.

In the era of breaking news, being second is often the same as being last. If an editorial team is forced into complex, manual workflows because of a fragmented tech stack, content reaches the market too late to capture peak search volume or social trends. This friction creates a culture of caution precisely when marketing needs a culture of velocity to capture organic traffic.

3. Tech Debt vs. Innovation.

Tech debt is the future cost of rework created by choosing “quick-and-dirty” solutions. This is a silent killer of marketing budgets. Every hour an engineering team spends fixing plugin conflicts or managing security fires caused by a cobbled-together infrastructure is an hour stolen from innovation.

Conclusion: Trading Toil for Agility

Ultimately, shifting to a unified standard is about reducing inefficiencies caused by “fighting the tools.” By removing the technical toil that typically hides insights in siloed tools, media organizations can finally trade operational friction for strategic agility.

When your site’s foundation is solid and fast, editors can hit “publish” without worrying about things breaking. At the same time, marketers can test new ways to grow the audience without waiting weeks for developers to update code. This setup clears the way for everyone to move faster and focus on what actually matters: telling great stories and connecting with readers.

The era of stitching software together with “sticky tape” is over. For modern media companies to thrive amid constant digital disruption, infrastructure must be a launchpad, not a hindrance. By eliminating the Fragmentation Tax, marketing leaders can finally stop surviving and start growing.

Jason Konen is director of product management at WP Engine, a global web enablement company that empowers companies and agencies of all sizes to build, power, manage, and optimize their WordPressⓇ websites and applications with confidence.

Break Free from the Fragmentation Tax

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May 20 2026

Can A 300,000-Influencer Network Built On AI-Generated Content Work? via @sejournal, @gregjarboe

When Unilever CEO Fernando Fernández stood before investors and declared that the era of expensive corporate brand advertising was over, calling traditional TV-heavy campaigns “lazy marketing,” the shockwave through the agency world was immediate. Half of Unilever’s massive global advertising budget would shift to a “social-first” strategy. Creator collaborations would scale by 20 times. The target would be an army of over 300,000 influencers, including a micro-influencer in every postal code in key markets like India.

Traditional advertising agencies that had spent decades building relationships around six-figure production budgets and a handful of celebrity partnerships suddenly faced a client with an operationally impossible mandate. Manual sourcing, onboarding, and content approval at 300,000-creator scale simply does not exist as a human workflow. Specialized creator agencies picked up business that legacy agency-of-record relationships had assumed were locked in.

The panic was understandable. It was also aimed at the wrong target.

The More Important Question

A March 2026 Adobe Express study surveyed video creators across YouTube, TikTok, and Instagram and found that 71% have now adopted AI video generation or editing tools. Of those, 41% deploy them on a weekly basis. 56% of creators using AI tools report saving over 30 minutes per video on average, with 10% shaving more than four hours off their production time. On the performance side, they’re seeing a 19% average increase in audience watch time and a 17% boost in community engagement. Half plan to increase their AI tool spending over the next year.

So, Unilever is building an army of 300,000 creators, and 71% of creators are now using AI to produce their content. The math is straightforward, and what Unilever is actually building is a massive distributed network for the production and distribution of AI-assisted content at a scale the marketing industry has never seen.

The question that hasn’t been answered yet is whether any of it will work.

Will It Work?

Unilever’s 300,000-creator network is generating content at a scale that makes traditional test-and-learn frameworks difficult to apply cleanly. When hyper-local micro-influencers are producing AI-assisted videos for niche audiences across hundreds of markets simultaneously, the signal-to-noise problem becomes acute. Individual pieces of content may perform well in isolation while the overall brand narrative diffuses into incoherence. Or the personalization may be exactly what audiences want, and the aggregate effect may be stronger than anything a single high-production campaign could achieve. Right now, the honest answer is that nobody knows with confidence.

Where DAIVID And ADIN.AI Come In

On April 27, 2026, two companies that many SEO professionals and digital marketers haven’t heard of yet announced a partnership that addresses the exact problem Unilever’s strategy creates.

DAIVID is a creative intelligence platform whose AI models, trained on tens of millions of human responses to ads, predict in seconds how any piece of ad creative will perform – measuring attention, 39 distinct emotions, memory encoding, brand recall, and likely next-step actions – without requiring human panels. ADIN.AI is an AI-native operating system for enterprise marketing that sits above an organization’s existing tools and provides a unified intelligence layer across channels, budgets, and decisions.

The partnership embeds DAIVID’s creative effectiveness models directly into ADIN.AI’s platform, creating what they describe as a live loop between creative intelligence and media execution. Before a campaign launches, marketers can identify which creative is most likely to succeed and allocate budget accordingly. While campaigns run, they can scale high-performing assets and pause underperformers in real time. After campaigns end, the historical performance data becomes benchmarks that guide future creative and media planning.

Ian Forrester, CEO of DAIVID, described the core problem the partnership solves: “Creative is a key driver of advertising outcomes, but for too long it has been measured in isolation, disconnected from media results.” The first live client is Ajinomoto, the global food and nutrition company.

Why This Matters For SEO And Digital Marketing Professionals

The traditional advertising agency’s anxiety about Unilever’s creator pivot was understandable but slightly misdirected. The real disruption isn’t that Unilever is working with 300,000 influencers instead of three ad agencies. The real disruption is that when 71% of those creators are using AI tools to produce content at speed, and that content is being distributed across dozens of platforms in hundreds of markets simultaneously, the evaluation infrastructure that used to separate good creative decisions from bad ones stops working.

Human panels are too slow. A/B testing individual pieces of content across a 300,000-creator network is logistically impossible. Traditional brand-tracking surveys capture what happened last quarter, not what’s working right now.

What DAIVID and ADIN.AI are building is the kind of infrastructure that makes the Unilever model actually governable – a system that can score creative at scale, link those scores to media performance in real time, and surface the signal from the noise before the budget has already been allocated to the wrong places.

Shelley Walsh made the point in her recent Search Engine Journal article on AI content scaling that enterprise brands face a specific trap: They know what they want to do (scale content production) but not how to do it without sacrificing the quality signals that make the content worth producing. The DAIVID and ADIN.AI partnership doesn’t solve the content quality problem. But it does solve the evaluation problem – which is arguably more urgent when you’re managing 300,000 creators rather than three.

For SEO professionals and content marketers, the practical implication is familiar. The distribution channels are changing, the production tools are changing, and the volume is increasing. What stays constant is the need to measure what’s actually working and make decisions based on that measurement rather than assumptions. That’s true whether you’re optimizing for search citations or creator content performance. Ground truth it, as always.

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Featured Image: elenabsl/Shutterstock

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May 20 2026

Google Ads Budget Misallocation Is More Common Than You Think – And Harder To Spot via @sejournal, @LisaRocksSEM

Every advertiser, from small businesses to enterprises, can struggle with knowing if their budget is allocated for the best results. Budget allocation used to be more straightforward, but campaign spend has shifted, and a lot of accounts could use a second look.

Performance Max has disrupted how budget flows through accounts in new ways over the past few years. Advertisers who set up their campaign structure without considering PMax are running budgets against a different landscape than what they originally designed for.

Drawing from patterns I see consistently across accounts, here are three ways Google Ads budget gets misallocated across campaign types and how to diagnose what’s happening in your own account.

Reason 1: Low Budgets Restrict Smart Bidding

Smart Bidding is basically an exercise in pattern recognition. When a campaign has low conversion volume, the algorithm is forced to make decisions based on a small data set rather than meaningful trends. This leads to unpredictable performance swings and bid-shunting, where the system pulls back spend because it lacks the information to enter competitive auctions.

1. The Cold Start Myth

For years, the prevailing wisdom was that Smart Bidding required a warm-up period of manual bidding to prime the account with data. Google has officially retired this requirement, and Search Engine Journal’s coverage of Google’s Smart Bidding clarification confirms this shift. The algorithm now uses cross-campaign learning and contextual signals like device type and time of day to begin optimizing immediately upon launch.

Starting and optimizing are not the same thing, though. While a cold start is possible, the algorithm still requires a steady stream of ongoing data to calculate its bids against real-world performance. Without this, the campaign stays in a perpetual learning state, and the ad manager has problems scaling.

2. The Campaign Vs. Account Threshold

A common mistake for ad managers is evaluating conversion volume at the account level. Google’s internal recommendations emphasize that thresholds for stability apply at the campaign level. According to official best practices:

For Target CPA: A campaign should ideally see at least 30 conversions in the last 30 days.
For Target ROAS: A minimum of 50 conversions in the last 30 days is recommended for the algorithm to accurately predict future conversion value.

Dividing a budget across three campaigns, each generating 15 conversions, is not mathematically the same as one campaign generating 45. In that fragmented scenario, the machine learning operates within three isolated silos, each struggling to reach a statistical significance high enough to make aggressive bidding decisions. This often results in budget throttling, where a campaign fails to spend its daily budget because the algorithm is holding back on serving.

What To Prioritize: Strategic Consolidation And Bid Floor Alignment

To optimize a low-volume account, ad managers should restructure smaller campaigns to consolidate into fewer, larger campaigns, for modern bidding success:

Consolidate for Conversion History: Combine smaller campaigns into larger campaigns. This is the fastest way to push a campaign forward. By pooling data, you can give the algorithm enough conversion history it needs to identify winning signals and exit the learning phase faster. Google’s own stance on campaign consolidation reinforces this approach, noting that consolidation is now a core recommendation for stable Smart Bidding performance.
Change to Maximize Strategies: If volume is consistently low, switch from Target bidding (tCPA/tROAS) to Maximize Conversions or Maximize Conversion Value. These strategies are more forgiving because they prioritize spending the budget to find the best available opportunities rather than restricting spend to hit a rigid efficiency metric the algorithm doesn’t yet have the data to guarantee.
The 10x Rule for Stability: To keep the algorithm from restricting delivery, ensure your daily budget is at least 10x your Target CPA. As explored in this breakdown of why budgets overspend even with a Target ROAS or CPA in place, setting a budget too close to your target, such as a $50 tCPA on a $60 daily budget, limits the algorithm’s ability to enter auctions, leading to stagnant spend and missed targets.

Reason 2: Performance Max Overspending Budget

The core problem with PMax is that it’s basically a black box for incrementality. In PPC, incrementality measures true lift, meaning the conversions that happened because of your ad and wouldn’t have occurred otherwise. Because PMax is built to maximize conversion value, it often can’t tell the difference between a net-new customer and someone who was already going to buy from you.

1. The Brand Traffic Problem

Branded queries have the highest intent and the lowest CPA in most accounts. PMax tends to go after them aggressively because they’re easy wins that help hit ROAS targets. From the dashboard, the campaign looks like it’s crushing it. What’s actually happening is that PMax is intercepting traffic that a lower-cost branded search campaign or your organic listing would have captured anyway.

That’s not incremental revenue. You’re paying a premium for a customer who was already knocking on your door, and it inflates CPCs on terms you already own.

Google recognizes the overlap between PMax and Branded Search, recommending Brand Exclusions as the primary tool for advertisers to maintain control over brand-specific traffic and avoid redundant costs.

2. The Zombie Logic (Underperforming Offers)

PMax funnels budget toward products with strong conversion history and largely ignores everything else. New launches and niche SKUs with limited data get almost no impressions. Ad managers who think they’re running a full-catalog campaign often find, after auditing the Listing Groups, that PMax has been directing the majority of spend toward a small slice of top performers the whole time.

While the industry uses the term “Zombie Products,” Google addresses this directly in its Retailer Best Practices. Google advises managers to monitor the Product Issues column for underperforming offers. To ensure full-catalog coverage, Google suggests using Custom Labels to segment high-priority or low-velocity products into separate campaigns, preventing the algorithm from starving niche inventory of budget.

3. The 2024 Auction Shift: From Priority To Ad Rank

Historically, PMax held absolute priority over Standard Shopping. If a product existed in both campaign types, PMax won the auction automatically. As of October 2024, that rule is gone. Google Ads Liaison Ginny Marvin confirmed that normal auction dynamics now apply: the campaign with the highest Ad Rank serves.

Google’s second-price auction means you won’t directly bid against yourself in a way that inflates your own CPC, but running overlapping campaigns can still create budget unpredictability and complicate attribution. Without the PMax priority rule, you can no longer guarantee which campaign type will win the auction for a specific product. That makes it very hard to run clean tests because both campaign types are now competing for the same user intent.

What To Prioritize: Taking Back Budget Control

The fix here is moving beyond a set-it-and-forget-it PMax setup:

Implement Brand Exclusions: Use Brand Settings at the campaign level, or account-level negative keyword lists, to block PMax from bidding on your brand terms. As I covered previously in my analysis of AI-driven budget rebalancing, branded queries carry the highest intent but the lowest incremental value. Brand exclusions push the algorithm toward true prospecting, where AI actually adds value.
Activate New Customer Acquisition Goals: The new customer acquisition goal setting tells PMax to bid more aggressively for new users. This shifts the focus from total attributed ROAS to incremental growth, so the budget is working to find people who haven’t bought from you before.
Segment by Product Volume: Move low-data products out of your main PMax campaign and into a separate PMax campaign or a Standard Shopping campaign with manual bids. This keeps budget from concentrating on your top 5% of SKUs while everything else gets ignored.
Clean Up Campaign Structure: With PMax priority gone, use Negative Keyword Themes and Product Filters to explicitly separate PMax and Standard Shopping. Letting Ad Rank sort traffic between the two leads to unpredictable and messy reporting. Clean segmentation is the only way to get reliable data.

Reason 3: Why Your Budget Is Sitting In Non-Converters

One critical mistake an ad manager can make is cutting budget from campaigns that show zero or low conversion value. On a standard last-click dashboard, this is a smart optimization. In reality, this can lead to account-wide performance decline.

1. The End of Rule-Based Attribution

In late 2023, Google officially deprecated all rule-based attribution models, including First-click, Linear, Time Decay, and Position-based. All conversion actions were migrated to Data-Driven Attribution.

Data-Driven Attribution uses AI to assign fractional credit across the entire customer journey. A campaign that shows zero conversions on a last-click basis might have influenced a final sale on a different traffic source. Cut that budget and you’re cutting the assist that your top-performing campaigns rely on to close the conversion.

2. The Signal Loss Chain Reaction

Smart Bidding requires a constant stream of signals to identify who to bid on. Upper-funnel and discovery campaigns often provide the first touchpoint that qualifies a user.

When you pause an underperforming campaign, you create a signal gap. Because of conversion lag, the time it takes for a user to convert after their first interaction, you may not see the impact of this budget cut for 7 to 14 days. As outlined in this guide to PPC budget strategies across campaign stages, pausing campaigns for extended periods can damage algorithm performance upon restart, potentially taking weeks to recover historical context. By the time your best campaigns start to decline, you’ve likely forgotten the budget decision that caused it.

What To Prioritize: Audit The Assists Before You Cut

Before you reallocate budget from a low-conversion campaign, verify its true hidden value using these two diagnostic checks:

The Google Ads Attribution Report: Navigate to Goals > Measurement > Attribution. Use the Model Comparison tool to compare Last Click against Data-Driven. If the campaign shows a significantly higher conversion value under the Data-Driven model, it is an essential part of your funnel and should not be paused.
The GA4 Advertising Report: Access the Google Analytics 4 Model Comparison report to see how your campaigns interact across channels. GA4’s Conversion Paths visualization lets you see exactly where a low-converting campaign sits in the early or mid-stages of the journey.

The rule of thumb: If a campaign has high assisted conversions but low direct conversions, treat it as a feeder campaign. Instead of pausing it, move it to a lower maintenance budget to keep the data signals flowing to your PMax and Search campaigns.

Before You Move Budget, Run These 3 Checks

Before you shift any spend, run through three quick checks.

Does each campaign have enough conversion volume to support its current bidding strategy?
Is PMax running Brand Exclusions and a New Customer Acquisition goal?
Before pausing anything for low conversion value, have you checked the GA4 Model Comparison report?

If you can answer yes to all three, your budget is likely in the right place.

The accounts I see perform best aren’t necessarily top-tier spenders. They’re better structured, and designed with a specific purpose for each campaign.

More Resources:

Featured Image: Roman Samborskyi/Shutterstock

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May 20 2026

Colossal Biosciences is growing chickens in a 3D-printed artificial eggshell

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Artificial eggshell, not artificial egg: Colossal Biosciences has grown baby chicks inside 3D-printed plastic containers coated with a silicone-based membrane that mimics an eggshell’s oxygen exchange — a meaningful step, but scientists say the company is overselling it.

The moa is one target: Colossal’s goal is resurrecting the giant moa, a 12-foot flightless bird hunted to extinction — which would require genetically rewriting thousands of DNA letters and scaling up the artificial eggs to the size of a salad spinner.

Scientists are skeptical: Researchers have been growing birds in artificial containers since 1998 and say Colossal’s claims of a first-ever breakthrough are overblown — a familiar pattern for a company that last year also faced widespread rejection of its “dire wolf” resurrection claim.

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The baby chicks were shifting and starting to pip—or trying to hatch. But not from an egg.

Instead, these chickens were growing inside transparent 3D-printed plastic cups at the Dallas headquarters of Colossal Biosciences.

The biotech company today claimed it has developed a “fully artificial egg” as part of its effort to resurrect extinct avian species, including birds like the dodo and the giant moa.

But “artificial eggshell” would probably be a better description for the invention. It’s an oval-shaped printed lattice, coated inside with a special silicone-based membrane that lets in oxygen, just as a real eggshell does.

To generate birds, Colossal took recently laid chicken eggs and carefully poured their contents into the artificial shells, where they continued growing. A window on top lets researchers peek inside.

“To see them all moving around in their artificial eggs was absolutely mind blowing,” says Andrew Pask, the company’s chief biology officer. “You really feel you can grow life outside of the womb.”

Colossal was founded in 2021 with plans to use gene editing and reproductive technology to restore extinct species, including the woolly mammoth. It’s since raised more than $800 million toward what it now terms the “scalable and controllable” creation of animals.

According to Pask, the egg technology could help conserve at-risk bird species. It could also play a role in a project to re-create the extinct giant moa, a flightless 12-foot-tall bird that once lived in New Zealand and laid four-liter eggs, larger than those of any living bird.

But Colossal may be able build one that’s big enough. The company provided a photograph of a prototype 3D-printed egg so large that staff have started to call it the “salad spinner.”

The moa went extinct after canoes carrying the ancestors of the Maori arrived on New Zealand’s South Island about 750 years ago. Archeological sites showcase the birds’ bones alongside stone cutting tools—clear evidence that they were hunted.

To be clear—Colossal isn’t close to re-creating the moa. Before that could happen, scientists would need to study DNA data from old moa bones and insert thousands of genetic changes into the genome of an existing bird, something that’s still technically difficult to do—with or without an artificial egg.

Some scientists also think Colossal is taking too much credit for its artificial eggshell, which it announced in a thundering YouTube video intoning that the company has solved the “impossible question of which came first, the chicken or the egg.”

The video is pure Hollywood—it’s meant to be funny and exciting. But Colossal has a habit of antagonizing scientists by making false and exaggerated claims. Last year, for instance, the company said it had re-created the extinct dire wolf—a claim widely rejected by experts.

This time, Colossal’s fluffed-up assertion of having created the “first-ever shell-less incubation system” is what’s raising hackles among the small flock of scientists who’ve been working on the technology for years.

“Clearly an overstatement,” says Katsuya Obara, at the University of Tsukuba in Japan, who in 2024 hatched chickens from beneath transparent plastic film. “The technology here is essentially a modification of existing methods.”

In fact, Obara notes, growing birds in artificial containers goes all the way back to 1998, when another Japanese group managed to do it with quail.

What may be an advance by Colossal is the special membrane, which lets the embryo access more oxygen. Previous systems required scientists to supplement the gas—something that may not have been good for the chicks, as often some of them would fail to hatch.

The work on the artificial eggshell was carried out in Dallas by Colossal’s exogenous development team, or Exo Dev. That group is also trying to develop artificial wombs for mammals, starting with marsupials.

“We’re looking at every single facet of what’s happening during a mammalian pregnancy to unpack exactly how we then go about recapitulating that,” says Pask.

For that team, an artificial eggshell is a relatively quick and easy technical win. That’s because chickens are already an example of ex utero development. After an egg is laid, a small embryo sitting on top of the yolk starts growing, drawing nutrients from the yolk, the white, and even the shell, which provides calcium. (Colossal says it has to add ground-up calcium to the artificial eggs.)

looking down into the artificial egg shell to see a developing chick embryo and its vascular structure

In order to create a moa, Colossal will have to genetically alter another type of bird, changing potentially thousands of DNA letters. But so far, chickens are the only bird species that can be genetically engineered. And that’s via a tricky process of editing stem cells that produce egg and sperm. Scientists have to add or delete DNA letters from these cells and then inject them back into an egg. The resulting bird will carry the genetic changes in its gonads—and then be able to pass them on.

Pask says Colossal’s idea is that it could modify avian stem cells enough to produce moa-like sperm or eggs. But then you might have the odd situation of a chicken laying an egg with a moa embryo inside it. “You would have chickens making moa egg and moa sperm. But it’s still a chicken egg,” he says.

Helen Sang, a professor emeritus at the Roslin Institute in the United Kingdom, says she’s not sure a moa embryo could survive on the yolk of a chicken egg, given evolutionary differences. “There are significant challenges to overcome to grow an embryo of a different species in artificial eggs,” says Sang.

Just one of those is the huge size discrepancy. The amount of yolk in a chicken egg would hardly be enough to support the much larger moa chick. Yet Pask says that is exactly where the artificial egg will come in handy.

He says it may be possible to use a fine needle to slowly “put 50 yolks together to make that yolk mass much larger.”

“The chicken egg isn’t going to be big enough to support the growth of the moa through to term, to when it would normally hatch, but that’s when you could then take that egg, put it into the artificial egg environment, and then scale it up in size,” he says.

So far, Pask says, the artificial egg is working well for chickens—almost too well. “We hatched 26 chickens and then [our CEO] asked us to put the brakes on. We have too many chickens running around.”

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May 20 2026

The Download: Musk v. Altman, smart glasses for warfare, and Google I/O

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

Here’s why Elon Musk lost his suit against OpenAI

Elon Musk has lost his lawsuit against OpenAI, which centered on whether the company breached its founding contract as a nonprofit. A jury found that he sued too late, meaning his claims are barred by statutes of limitations. But the verdict didn’t judge if OpenAI violated its nonprofit mission—only whether Musk brought the case in time.

The dispute centers on when OpenAI began shifting toward a for-profit structure. The company argued that signs of a shift were visible as early as 2017, while Musk said he only discovered the change in 2022.

Here’s a closer look at the timeline, why Musk lost, and why the fight over OpenAI’s structure may not be over.

—Michelle Kim

Join us later today for a subscriber-only Roundtables discussion about what happened in the courtroom and what the verdict means for OpenAI and the larger AI race. Register here.

Inside Anduril and Meta’s quest to make smart glasses for warfare

The defense-tech company Anduril has shared new details about the augmented-reality headset for the military it’s prototyping with Meta, including a vision for ordering drone strikes via eye-tracking and voice commands.

Quay Barnett, who leads the effort at Anduril following a career in the Army’s Special Operations Command, says he aims to optimize “the human as a weapons system.” Find out how he plans to do it—and what smart glasses could mean for warfare.

—James O’Donnell

What to expect at Google I/O this week

When Google opens its doors today for its annual developer conference, I/O, it will do so as a clear third place in the foundation model race.

A foundation model’s reputation these days rests largely on its coding capabilities, and for months Google’s coding tools have been outgunned by Anthropic’s Claude Code and OpenAI’s Codex. But the company still shapes the cutting edge in areas such as AI for science. At I/O this week, it will try to prove it can compete on both fronts.

I’m going to be at Mountain View this week to see what goes down. Here are three things to keep a close eye on.

—Grace Huckins

This story is from The Algorithm, our weekly newsletter giving you the inside track on all things AI. Sign up to receive it in your inbox every Monday.

Can AI learn to understand the world?

As the limits of LLMs become clearer, researchers are developing a new kind of AI designed to understand the physical environment: world models.

Recent developments from Google DeepMind, Fei-Fei Li’s World Labs, and Yann LeCun’s new startup have pushed these systems to the forefront of AI. At an upcoming virtual event, MIT Technology Review will examine the progress—and what comes next.

On Thursday, May 21, editor in chief Mat Honan, senior AI editor Will Douglas Heaven, and AI reporter Grace Huckins will take part in an exclusive Roundtables discussion on world models. Register here to join the session at 19:30 GMT / 2:30 PM ET / 11:30 AM PT.

World models are one of our 10 Things That Matter in AI Right Now, a new guide to the technologies and ideas shaping the future of AI.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 OpenAI’s legal win over Elon Musk clears its path to a blockbuster IPO
The jury’s verdict was a critical moment for the company’s future. (Reuters $)
+ The trial spilled plenty of dirt about Silicon Valley. (MIT Technology Review)
+ And added to concerns about AI’s leadership. (The Verge)

2 Google and Blackstone are launching a new AI cloud company
The venture will use Google’s specialized chips. (Bloomberg $)
+ It aims to mount a challenge to Nvidia. (FT $)
+ Blackstone is investing $5 billion in the company. (WSJ $)

3 Meta is reshaping its workforce around AI while preparing deep layoffs
It’s reassigning 7,000 employees to four new AI-focused groups. (NYT $)
+ And plans to lay off 10% of its staff on Wednesday. (Reuters $)
+ More cuts are expected later this year. (CNBC)

4 The Iran conflict is straining the AI supply chain
TSMC, Foxconn, and Infineon have felt major disruption. (CNBC)
+ The war also threatens a vital water technology. (MIT Technology Review)

5 China’s AI-powered brain implants are moving to real-world use
Some devices will soon be sold to the public. (Nature)
+ BCIs now must be proven as products. (MIT Technology Review)

6 A US cybersecurity agency exposed its own digital keys on GitHub
A researcher said it’s the worst leak he’s ever seen. (Krebs on Security)
+ The culprit was the CISA, a relatively new branch of the DHS. (Gizmodo)

7 Supercharging immune cells may help control HIV long-term
CAR-T cell therapy is showing promise for managing HIV. (Wired $)

8 Filipino virtual assistants are powering “thought leadership” on LinkedIn
Low-paid workers use AI to write posts for Western executives. (Rest of World)

9 Big Four accounting firms have more job ads for AI staff than auditors
Accounting giants are rushing to adapt to technological disruption. (FT $)

10 Tech founders are being sent to etiquette school
In the AI era, soft skills may matter more than ever. (WSJ $)

Quote of the day

“Shit, I should have asked for more.”

—President Trump tells Fortune that he should have requested a greater share of Intel than the 10% stake that the US government received.

One More Thing

Think that your plastic is being recycled? Think again.

On a kayak trip through a Connecticut salt marsh, plastic waste appears almost immediately. There are bags in reeds, bottles in the water, and tiny pieces scattered everywhere. What looks like a pristine ecosystem is already saturated.

Plastic is produced at enormous scale but rarely recycled. Instead, it breaks apart into microplastics, which are now detected across the environment and in human bodies.

Read the full story on why plastic pollution is so hard to contain.

—Douglas Main

We can still have nice things

A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)

+ Discover the beauty hidden within numbers at this charming site.
+ Find out how many millions of miles you’ve traveled through space since birth.
+ An extraordinary image has captured the split-second the ISS silhouetted itself against the Moon.
+ The most insane megaproject you’ve never heard of tried to turn atomic bombs into peaceful construction tools.

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May 20 2026

Understanding the modern cybercrime landscape

Throughout 2025, HPE observed significant changes in how cybercriminals operate. Analyzing real-world threats, our HPE Threat Labs highlighted an industrialization of the cyber criminals’ methods in its new In the Wild Report, enabling greater scale, speed and structure in their campaigns. They typically use automation and AI to exploit longstanding vulnerabilities, and many have adopted a professional, corporate hierarchy to optimize their efficiency.

Cybersecurity threats today are as menacing as ever for enterprises, as any CISO or CIO can probably confirm. But, digging behind that straightforward statement, there is a much more nuanced, complex cybersecurity landscape at play. This can make it significantly harder to plan, execute, and sustain effective strategies and solutions to protect the network—plus the often valuable—sometimes priceless—data, apps, and assets it transports and stores.

But it can be done, with the right philosophy and strategy, and the right tools and insights.

We must first understand the contemporary cybersecurity landscape. This understanding can unlock the right strategy and then onward to identify the tools and insights necessary to protect an enterprise’s network effectively.

There are five primary factors influencing the landscape, some old, some new, all dynamic. These factors are distinct but often interdependent, both within themselves and with one or more of the others. Another meaningful way of looking at them is “internal” and “external”; as ever, understanding and dealing with what is in your control can also help to navigate and mitigate what is beyond your control.

Five key factors influencing today’s dynamic cybersecurity landscape

1. Expectations

The first factor is predicated on the fundamental reality of an enterprise’s reliance on its network. Most enterprises have already undergone some form of digital transformation and are reaping the day-to-day benefits. This means that the number of people, devices, and things using the network continues to grow; it also means that people’s expectations of the network are higher than ever before – they demand that it does exactly what they need it to do, typically across a proliferation of devices and from multiple locations. Conversely, many employees might not be fully aware of cyber threats and infiltration methods, so their skillsets can easily be the weak point that admits bad actors into the network.

Equally, senior management and board members have high expectations at a meta level. Embracing digital transformation and network reliance means the enterprise’s function and reputation are inextricably tied to that. Loss of reputation due to a security breach is a chilling prospect, as is the threat of financial penalty and revenue loss. So, in the minds of leadership, the network has to be safe from cyber threats and be compliant.

2. Financial pressures

The first factor arguably contradicts its neighbor in the landscape: general financial constraints and the pressure on CISOs and CIOs to achieve more with less. Despite the strategic reliance on the network and the expectation that it will be protected from cyber threats regardless, the appropriate latticework of defenses (e.g., skilled and right-sized IT teams using progressive tools and meaningful data insights, plus constant workforce education) is not always properly funded and sustained, particularly in the current tough economic climate.

3. Complex infrastructure operations

The ongoing pursuit of digital transformation and consequent network reliance also drives the third factor. Ironically, there is another facet of enterprise protection and financial control wrapped up in this. The widespread move from one-stop shops (avoiding IT vendor lock-in in favor of more competitive pricing and autonomy) has created a more complex, multivendor environment. This is coupled with multiple IT domains required to handle many diverse functions and layers of IT infrastructure (e.g., cloud, on-prem), all connected to the network. Complex, mission-critical IT operations now need to be monitored and protected from increasingly sophisticated cyber breaches.

4. Unpredictable geopolitics and economics

Shifting from the first three factors—all internal to an enterprise—the fourth is unquestionably external and without doubt the most intractable risk for any enterprise, individual, or industry group. Global uncertainty and tension are unavoidably putting even greater pressure on already-tight IT budgets, component supply chains and power costs. This can easily exacerbate existing constraints on cybersecurity budgets when vigilance and protection are more needed than ever. Unfortunately, in cyberspace one cannot always point a finger in one direction to identify an adversary. Geopolitical alliances in cyberspace are much more difficult to track, and defending against an escalating tension becomes an all-out fight to secure the network.

5. Evolving cyber threats

The fifth factor is obviously the epicenter of today’s cyber security landscape. According to the HPE Threat Labs’ report, governments were the most frequently targeted sector globally in 2025, followed by finance, technology, defense, and manufacturing. The prevailing global geopolitical and economic situation may further accelerate the twin motivations of nation state-linked espionage and organized crime for extortion and theft.

Use the network to protect the network… and beyond

The current cybersecurity landscape calls for a re-think of the network’s pivotal role and how it can manage an enterprise’s digital defenses effectively, dynamically, and comprehensively. Overall, the network can be an excellent security sensor and enforcement point, using built-in security capabilities rather than being a collection of devices with an inflexible, bolted-on security layer.

Much as cybercriminals use agentic and generative AI to intensify their campaigns, CISOs can stay ahead more easily by leveraging AI-driven network platforms for 24×7 automated management of security policy enforcement (e.g., zero trust), threat monitoring, and mitigation, encompassing devices, things, and users. Meaningful data insights can be harvested, analyzed, and recycled back into secure networking management tools for dynamic protection.

This approach helps the progressive enterprise to overcome increasingly sophisticated, multi-step, and prolific attacks, while better managing IT costs and simplifying oversight of IT operations. It can also significantly improve the user experience, going a long way to meet and even exceed those rising expectations consistently.

As a strategy in today’s uncertain world, embracing this self-driving network paradigm enables flexibility, visibility, and consistency in an enterprise’s frontline digital defenses.

For more, read the “In the Wild” report.

This content was produced by HPE. It was not written by MIT Technology Review’s editorial staff.

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Artificial intelligence Roundtables Subscriber-Only Stories Uncategorized

May 20 2026

Roundtables: Inside the Musk v. Altman Trial

Listen to the session or watch below

Elon Musk lost his suit against OpenAI, in which he alleged CEO Sam Altman and President Greg Brockman had deceived him over the company’s non-profit status.

Watch as AI reporter and attorney Michelle Kim, who covered the trial for MIT Technology Review, joins in conversation with editor in chief Mat Honan to go behind the scenes of the trial and the implications for the AI race.

Speakers: Mat Honan, Editor in Chief, and Michelle Kim, AI Reporter

Recorded on May 19, 2026

Related Stories:

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May 20 2026

E.U. Product Safety Laws Reach Sellers

The E.U.’s General Product Safety Regulation applies to digital commerce, global supply chains, and internet-connected devices.

First issued in 2001, the revised GPSR became effective in December 2024 and applies to all merchants selling into the E.U., including those based in the U.S.

Unlike sector-specific rules for toys or electronics, the GPSR is a broad framework covering products and safety obligations not fully addressed elsewhere, such as home goods, sports equipment, accessories, kitchen items, and lifestyle items.

Responsible Person

The most significant requirement for non-E.U. sellers is the obligation to designate an E.U.-based “Responsible Person,” sometimes called a Responsible Economic Operator.

Under the GPSR, non-E.U. manufacturers and merchants must ensure that someone inside the European Union is formally responsible for product safety compliance, such as an importer, authorized representative, fulfillment provider, distributor, or another company.

The Responsible Person’s contact details must appear on the product, packaging, or accompanying documentation.

The requirement is an operational hurdle for foreign merchants accustomed to shipping from domestic warehouses, who may now need E.U. based representation.

Listing Requirements

Safety-related information on ecommerce listings must be visible before purchase, not only on physical packaging.

Depending on the category, product listings may require the manufacturer’s name, the Responsible Person’s contact details, batch numbers or other identifiers, intended use, safety warnings, and care instructions. The requirement applies to products on Amazon, Etsy, eBay, or a merchant’s own ecommerce site serving E.U. customers.

For example, a product page on Amazon.de could include a dedicated “Product Safety” section, such as:

E.U. Responsible Person: Acme E.U. Compliance GmbH, Berlin, Germany, gpsr@acme-eu.com
Manufacturer: Acme Home Goods LLC, Austin, Texas
Product ID: ACG-2047
Warning: Keep away from children under 3 years.

Screenshot of an Amazon Responsible Person page

Amazon’s Responsible Person dashboard includes E.U.-based compliance contacts for eligible products. Image: Amazon Seller Central.

Marketplace Enforcement

Many merchants first encounter the GPSR through marketplaces, which must enforce its rules or face fines or sanctions. Effectively, marketplaces are now frontline compliance gatekeepers and routinely verify listings and request missing info.

In practice, merchants lacking required E.U. representation or documentation may face delistings before regulators contact them directly.

Traceability

The GPSR also strengthens traceability obligations.

Products must include identifying information that allows authorities and sellers to trace items through the supply chain and remove them quickly if safety issues emerge. Manufacturers must maintain technical files and related safety documentation for up to 10 years.

For merchants operating dozens or hundreds of SKUs, maintaining structured compliance records can become a substantial operational task.

Next Steps

Merchants selling into Europe should take three steps.

First, determine whether products fall within GPSR’s scope. Most non-food consumer goods do.

Second, appoint an E.U.-based Responsible Person and update product labels and listings with the required contact information.

Third, assemble and maintain technical documentation, including risk assessments, compliance records, and traceability information.

GPSR compliance is now a cost of doing business in Europe, similar to VAT registration or customs administration. Plan for GPSR before entering the market, not after.

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