Why Google Runs AI Mode On Flash, Explained By Google’s Chief Scientist via @sejournal, @MattGSouthern

Google Chief Scientist Jeff Dean said Flash’s low latency and cost are why Google can run Search AI at scale. Retrieval is a design choice, not a limitation, he added.

In an interview on the Latent Space podcast, Dean explained why Flash became the production tier for Search. He also laid out why the pipeline that narrows the web to a handful of documents will likely persist.

Google started rolling out Gemini 3 Flash as the default for AI Mode in December. Dean’s interview explains the rationale behind that decision.

Why Flash Is The Production Tier

Dean called latency the critical constraint for running AI in Search. As models handle longer and more complex tasks, speed becomes the bottleneck.

“Having low latency systems that can do that seems really important, and flash is one direction, one way of doing that.”

Podcast hosts noted Flash’s dominance across services like Gmail and YouTube. Dean said search is part of that expansion, with Flash’s use growing across AI Mode and AI Overviews.

Flash can serve at this scale because of distillation. Each generation’s Flash inherits the previous generation’s Pro-level performance, getting more capable without getting more expensive to run.

“For multiple Gemini generations now, we’ve been able to make the sort of flash version of the next generation as good or even substantially better than the previous generation’s pro.”

That’s the mechanism that makes the architecture sustainable. Google pushes frontier models for capability development, then distills those capabilities into Flash for production deployment. Flash is the tier Google designed to run at search scale.

Retrieval Over Memorization

Beyond Flash’s role in search, Dean described a design philosophy that keeps external content central to how these models work. Models shouldn’t waste capacity storing facts they can retrieve.

“Having the model devote precious parameter space to remember obscure facts that could be looked up is actually not the best use of that parameter space.”

Retrieval from external sources is a core capability, not a workaround. The model looks things up and works through the results rather than carrying everything internally.

Why Staged Retrieval Likely Persists

AI search can’t read the entire web at once. Current attention mechanisms are quadratic, meaning computational cost grows rapidly as context length increases. Dean said “a million tokens kind of pushes what you can do.” Scaling to a billion or a trillion isn’t feasible with existing methods.

Dean’s long-term vision is models that give the “illusion” of attending to trillions of tokens. Reaching that requires new techniques, not just scaling what exists today. Until then, AI search will likely keep narrowing a broad candidate pool to a handful of documents before generating a response.

Why This Matters

The model reading your content in AI Mode is getting better each generation. But it’s optimized for speed over reasoning depth, and it’s designed to retrieve your content rather than memorize it. Being findable through Google’s existing retrieval and ranking signals is the path into AI search results.

We’ve tracked every model swap in AI Mode and AI Overviews since Google launched AI Mode with Gemini 2.0. Google shipped Gemini 3 to AI Mode on release day, then started rolling out Gemini 3 Flash as the default a month later. Most recently, Gemini 3 became the default for AI Overviews globally.

Every model generation follows the same cycle. Frontier for capability, then distillation into Flash for production. Dean presented this as the architecture Google expects to maintain at search scale, not a temporary fallback.

Looking Ahead

Based on Dean’s comments, staged retrieval is likely to persist until attention mechanisms move past their quadratic limits. Google’s investment in Flash suggests the company expects to use this architecture across multiple model generations.

One change to watch is automatic model selection. Google’s Robby Stein described mentioned the concept previously, which involves routing complex queries to Pro while keeping Flash as the default.


Featured Image: Robert Way/Shutterstock

Are Citations In AI Search Affected By Google Organic Visibility Changes? via @sejournal, @lilyraynyc

recently wrote about an unconfirmed Google algorithm update that rolled out in mid-January 2026, which negatively impacted the organic search visibility of dozens of major brands. For most of the impacted sites I analyzed, the impact was disproportionately targeted to the company’s blog, or another folder containing informational articles and resources.

That same organic trajectory has continued into mid-February for all of the subfolders I analyzed, using the Sistrix U.S. Visibility Index:

Image Credit: Lily Ray

Zooming out, this is what the drops look like when you look at the visibility trends across the whole domains, not just the blogs:

Image Credit: Lily Ray

Here is another example of the visibility impact on the company blog for the biggest company in the list (in terms of both ARR and organic visibility):

Image Credit: Lily Ray

And this is what the impact looks like when you look at the company’s full domain’s visibility in organic search:

Image Credit: Lily Ray

Needless to say, these recent organic visibility drops were extreme, relative to the sites’ overall SEO trajectories over the past few years. Drilling down into 11 of the sites that saw extreme declines over the last month, I wanted to see if this new data could help answer another question:

Do drops in Google organic search visibility coincide with similar drops in AI search citations?

My working hypothesis is that these drops are no longer just isolated to traditional search. Instead, I suspect we will find that, for most LLMs, AI search citation trends mirror what happens in Google’s organic search results, for two reasons:

1. The Direct Pipeline: Google’s AI Ecosystem

For Google’s own AI products – AI Mode and Gemini – the correlation should be strongest. Presumably, Google is using its own index and top-ranking search results to formulate AI search responses; therefore, dropping in organic rankings should logically cause those pages to be cited and referenced less frequently in generative answers.

2. The Downstream Effects: Third-Party LLMs (ChatGPT & Perplexity)

The link between Google organic rankings and third-party LLMs like ChatGPT and Perplexity is more nuanced, as we don’t know exactly which search engines these LLMs are surfacing for web search.

While there is a growing body of evidence (and industry reporting) suggesting that ChatGPT likely scrapes Google during live web searches, we still technically lack official confirmation from the source. Perplexity, on the other hand, is currently believed to utilize the Brave Search API as a core part of its retrieval process, alongside its own specialized “PerplexityBot” crawler.

To test this out, I wanted to drill down into the subfolders that saw substantial visibility drops on Google in recent weeks, to see whether the trend line for AI search citations followed suit.

To start, I honed in on a list of 11 sites whose subfolders saw substantial organic traffic drops between January 20, 2026 and February 16, 2026.

I used the Ahrefs MCP server with Claude Cowork to pull in estimated global monthly organic traffic numbers for each path (subfolder) in the list. Because most of the traffic declines started around January 21, 2026, I pulled the projected monthly organic numbers for January 20, 2026 and the most recent date, February 16, 2026.

I also redacted the site names, leaving the name of the subfolder and a brief, anonymized summary about the company type and the subfolder’s purpose:

Image Credit: Lily Ray

These subfolders experienced anywhere from a -5.7% to -53.1% drop in estimated monthly organic search traffic since January 20th, 2026.

Using Ahrefs Brand Radar, you can drill down to see the number of AI search citations that a given subfolder has received across various LLMs over time. For example, here is the ChatGPT citation trend line for the first subfolder listed in the above table (U.S. data):

Image Credit: Lily Ray

This is the corresponding chart showing the organic traffic trend for this same subfolder, which began dropping around January 21, 2026:

Image Credit: Lily Ray

I used the Ahrefs MCP server with Claude Cowork to pull global traffic and citation data, and to analyze this same pattern for 11 of the subfolders that saw big drops.

Note on methodology: While 11 subfolders are a small sample size, I was specifically looking for a “clean” data set – subfolders experiencing a similar algorithmic demotion on Google during the unconfirmed January 2026 update. By narrowing the scope, I could better isolate whether a loss of traditional search visibility translates directly into a citation drop in AI search.

Below are the high-level summaries of how organic traffic and citation counts changed across Google and various LLMs, including AI Mode, ChatGPT, Perplexity, and Gemini:

Image Credit: Lily Ray
Image Credit: Lily Ray

Findings:

  • The data shows a broad decline in both SEO traffic & AI search citations: Every subfolder in the study (11 of 11) experienced a drop in both Google organic traffic and total AI search citations, with a significant average citation decline of -22.5%.
  • Google’s AI Mode (-23.8%) and ChatGPT (-27.8%) showed the most severe declines, closely mirroring the -26.7% average drop in organic traffic.
  • While Gemini also saw broad declines (10 of 11 sites), Perplexity proved to be the most resilient, with only 4 of the 11 sites seeing a drop and a much milder average change of -2.9%.
    • This data supports the theory that Perplexity is primarily using non-Google search surfaces to generate its responses.

Looking at the changes in estimated organic traffic for each subfolder compared to total AI search citations between January 20 and February 16, 2026, the correlation is clear: Significant losses in organic search visibility are almost universally mirrored by a corresponding decline in AI search citations.

Image Credit: Lily Ray

Drilling down into specific LLMs, including Google’s AI Mode, ChatGPT, Perplexity, and Gemini, shows how the decline was nearly universal for most platforms, whereas Perplexity frequently displayed a significant divergence, showing positive citation growth for the majority of the subfolders despite their organic traffic losses.

Image Credit: Lily Ray

ChatGPT (green) consistently shows the deepest declines across almost every subfolder – often exceeding AI Mode and Gemini. This is intriguing because ChatGPT isn’t a Google product, yet it appears more sensitive to these organic ranking shifts than Google’s own Gemini.

This appears to be another clue that ChatGPT is reliant on Google’s search index during retrieval.

AI Mode and Gemini tend to move in the same direction but not the same magnitude. Despite both being Google products, AI Mode declines are generally steeper than Gemini’s. This could suggest they weight or source from Google’s organic index differently – perhaps AI Mode is more tightly coupled to live SERP rankings while Gemini draws from a broader or cached knowledge base.

The few sites where Perplexity did decline (e.g., Site J, Site K) are also the ones showing relatively smaller organic drops. So even in the cases where Perplexity tracked downward, it doesn’t appear to be correlated with the severity of the Google organic loss – further evidence that Perplexity is likely pulling from a different retrieval pipeline.

The below table shows all the organic search vs. AI search citation data in one place:

Image Credit: Lily Ray

The table reveals a clear pattern: every subfolder that lost organic visibility on Google also saw a decline in total AI search citations, with an average drop of -22.5% across all LLMs.

ChatGPT was the most severely impacted platform, with citation declines reaching as high as -42.3% (Site E) and exceeding -34% for five of the eleven subfolders – often surpassing even the organic traffic loss itself.

Google’s AI Mode followed a similar trajectory, while Gemini showed more moderate declines across the board.

The most notable outlier is Perplexity, which actually showed citation growth for 7 of the 11 subfolders, reinforcing the theory that it retrieves from a non-Google search index.

Perhaps the most interesting finding is that ChatGPT – a non-Google product – appears more tightly coupled to Google’s organic rankings than Google’s own Gemini, suggesting that ChatGPT’s web retrieval pipeline is heavily dependent on Google’s search results.

One recommendation I’ve been making since AI search entered the SEO conversation is that you shouldn’t invest in AEO/GEO tactics that could be detrimental to SEO performance. For example, using hidden prompt injections, cloaking, or self-promotional listicles (tactics that some have advocated for to boost AI search visibility) might be temporarily beneficial for AI search, but could cause massive headaches with Google and Bing’s organic search ranking algorithms down the line.

Now, we have even more evidence that AI search is fundamentally connected to SEO performance: If you drop in organic search, you can likely expect a corresponding drop in citations not only from Google’s own AI search products, but from other LLMs like ChatGPT, which appear to also be heavily reliant on Google’s search results.

The one notable exception is Perplexity, which showed citation growth for the majority of subfolders hit by the recent algorithm update. That said, it’s important to weigh this against the scale of traffic and LLM usage at stake. According to a recent article by Similarweb, ChatGPT received 5.8 billion web visits in August 2025, compared to 148.2 million for Perplexity.

To add to this, when you factor in Google’s organic search traffic – which still dwarfs all the AI search platforms combined – the vast majority of your search-driven visibility across both search engines and AI chatbots is still flowing through a pipeline where Google’s rankings dictate the outcome.

For the past year, the SEO industry has been asking how closely traditional SEO and AEO/GEO are really tied together. I think this data helps answer that question: Not only is a strong SEO foundation critical for AI search visibility, but tactics that hurt your organic rankings can have a cascading negative impact on your AI search citations as well. In other words, the fastest way to lose visibility in AI search might be to lose it in Google first.

More Resources:


This post was originally published on Lily Ray NYC Substack.


Featured Image: PeopleImages/Shutterstock

Enterprise SEO Operating Models That Scale In 2026 And Beyond via @sejournal, @billhunt

Most enterprises are still treating SEO as a marketing activity. That decision, whether intentional or accidental, is now a material business risk.

In the years ahead, SEO performance will not be determined by better tactics, better tools, or even better talent. It will be determined by whether leadership understands what SEO has become and restructures the organization accordingly. SEO is no longer simply a channel but an infrastructure, and infrastructure decisions are leadership decisions.

The Old SEO Question Is No Longer Relevant

For years, executives asked a familiar question: Are we doing SEO well? Or even more simply, are we ranking well in Google? 

That question assumed SEO was something you did, summed up as a collection of optimizations, audits, and campaigns applied after the fact. It made sense when search primarily ranked pages and rewarded incremental improvements. The more relevant question today is different: Is our organization structurally capable of being discovered, understood, and selected by modern search systems?

That is no longer a marketing question. It is an operating model question because AI optimization must become a team sport.

Search engines, and increasingly AI-driven systems, do not reward isolated optimizations. They reward coherence, structure, intent alignment, and machine-readable clarity across an entire digital ecosystem. Those outcomes are not created downstream. They are created by how an organization builds, governs, and scales its digital assets.

What Has Fundamentally Changed

To understand why enterprise SEO operating models must evolve, leadership first needs to understand what actually changed in search.

1. Search Systems Now Interpret Intent Before Retrieval

Modern search systems no longer treat queries as literal requests. They reinterpret ambiguous intent, expand queries through fan-out, explore multiple intent paths simultaneously, and retrieve information across formats and sources. Content no longer competes page-to-page. It competes concept-to-concept.

If an organization lacks clear intent modeling, structured topical coverage, and consistent entity representation, its content may never enter the retrieval set at all, regardless of how optimized individual pages appear.

2. Eligibility Now Precedes Ranking

This shift also changed the sequence of how visibility is earned. Ranking still matters, particularly for enterprises where much of the traffic still flows through traditional results. But ranking now occurs only after eligibility is established. As search experiences move toward synthesized answers and AI-driven surfaces, eligibility has become the prerequisite rather than the reward.

That eligibility is determined upstream by templates, data models, taxonomy, entity consistency, governance, and workflow design. These are not marketing decisions. They are organizational ones.

3. Enterprise SEO Has Crossed An Infrastructure Threshold

Enterprise SEO has always depended on infrastructure. What has changed is that modern search systems no longer compensate for structural shortcuts. In the past, rankings recovered, signals recalibrated, and messiness was often forgiven.

Today, AI-driven systems amplify inconsistency. Retrieval becomes selective, narratives persist, and structural debt compounds. Delivering results aligned to real searcher intent has shifted from a forgiving environment to a selective one, where visibility depends on how well the underlying system is designed. Taken together, these conditions define what a scalable enterprise SEO operating model actually looks like, not as a team or function, but as an organizational capability.

The Leadership Declaration: What Must Be True In 2026

Organizations that scale organic visibility in the coming years will share a small set of non-negotiable characteristics. These are not best practices. They are operating requirements.

Declaration #1: SEO Must Be Treated As Infrastructure

SEO must be treated as infrastructure. That means it moves from a downstream marketing function to a foundational digital capability. SEO requirements are embedded in platforms, standards are enforced through templates, and eligibility is designed before content is commissioned. When failures occur, they are treated like performance or security issues, not optional enhancements. If SEO depends on post-launch fixes, the operating model is already broken.

Declaration #2: SEO Must Live Upstream In Decision-Making

SEO must live upstream in decision-making. Search performance is created when decisions are made about site structure, content scope, taxonomy, product naming, localization strategy, data modeling, and internal linking frameworks. SEO cannot succeed if it only reviews outcomes; it must help shape inputs. This does not mean SEO dictates solutions. It means SEO defines non-negotiable discovery constraints, just as accessibility, performance, and security already do.

Declaration #3: SEO Requires Cross-Functional Accountability

SEO requires cross-functional accountability. Visibility depends on development, content, product, UX, legal, and localization teams working in concert, similar to a professional sports team. In most enterprises, SEO is measured on outcomes while other teams control the systems that produce them. That accountability gap must close. High-performing organizations define shared ownership of visibility, clear escalation paths, mandatory compliance standards, and executive sponsorship for search performance. Without this, SEO remains a negotiation rather than a capability.

Declaration #4: Governance Must Replace Guidelines

Governance must replace guidelines. Guidelines are optional; governance is enforceable. Scalable SEO requires mandatory standards, controlled templates, centralized entity definitions, enforced structured data policies, approved market deviations, and continuous compliance monitoring. This demands a Center of Excellence with authority, not just expertise. SEO cannot scale on influence alone.

Declaration #5: SEO Must Be Measured As A System

Finally, SEO must be measured as a system. Executives need to move beyond quarterly performance questions and instead assess structural eligibility across markets, intent coverage, entity coherence, template enforcement, and where visibility leaks and why. System-level measurement replaces page-level obsession.

This shift mirrors a broader issue I explored in a previous Search Engine Journal article on the questions CEOs should be asking about their websites, but rarely do. The core insight was that executive oversight often focuses on surface-level outcomes while missing systemic sources of risk, inefficiency, and value leakage.

SEO measurement suffers from the same blind spot. Asking how SEO “performed” this quarter obscures whether the organization is structurally capable of being discovered and represented accurately across modern search and AI-driven environments. The more meaningful questions are systemic: where visibility leaks, which teams own those failure points, and whether the underlying architecture enforces consistency at scale.

Measured this way, SEO stops being a reporting function and becomes an early warning system for digital effectiveness.

The Operating Model Divide

Enterprises will fall into two groups.

Some will remain tactical optimizers, where SEO lives in marketing, fixes happen after launch, paid media masks organic gaps, and AI visibility remains inconsistent. Others will become structural builders, embedding SEO into systems, defining requirements before creation, enforcing governance, and earning consistent retrieval and trust from AI-driven platforms.

The difference will not be effort. It will be organizational design.

The Clarifying Reality

Ranking still matters, particularly for enterprises where a significant share of traffic continues to flow through traditional results. What has changed is not its importance, but its position in the visibility chain. Before anything can rank, it must first be retrieved. Before it can be retrieved, it must be eligible. And eligibility is no longer determined by isolated optimizations, but by infrastructure – how content is structured, how entities are defined, and how consistently signals are enforced across systems.

Every enterprise already has an SEO operating model, whether it was designed intentionally or emerged by default. In the years ahead, that distinction will matter far more than most organizations expect.

SEO has become infrastructure. Infrastructure requires leadership because it shapes what the organization can reliably produce and how it is perceived at scale. The companies that win will not be the ones that optimize harder, but the ones that operate differently, by designing systems that search engines and AI-driven platforms can consistently discover, understand, and trust.

More Resources:


Featured Image: Anton Vierietin/Shutterstock

How To Set Up AI Prompt Tracking You Can Trust [Webinar] via @sejournal, @lorenbaker

Getting Real About AI Visibility Tracking

If you’re on the search or marketing team right now, you’ve probably been asked some version of: “Are we showing up in ChatGPT?” or “What’s our visibility in AI Overviews?”

And honestly? Most of us are still figuring that out.

Answer engines like ChatGPT, Perplexity, and Google AI Overviews have changed how people discover and evaluate solutions. Yet, we still see a lot of teams approaching AI visibility tracking the same way they’ve approached keyword tracking, and they’re just not the same.

Improper tracking leads to bad data that’s being used to make decisions. And bad decisions can be expensive.

That’s why we’re bringing in Nick Gallagher, Sr. SEO Strategy Director at Conductor, to walk through how to set up AI prompt tracking the right way. The goal is to walk away with a tracking framework you can actually trust.

What You’ll Learn

  • How AI prompt tracking works, and why the setup matters more than the volume of prompts you’re monitoring.
  • Best practices for choosing the right topics, prompts, and answer engines to track.
  • How to avoid common mistakes that lead to inaccurate or misleading AI visibility data.

Why This Matters Right Now

A lot of the conversations I’ve been having with SEOs and in-house marketers lately come back to the same thing: they know AI search is important, but they don’t trust the data they’re getting. Nick is going to break down why that’s happening and give you a clear framework to fix it for smarter decision-making. 

If you’re trying to measure AI visibility and want to make sure you’re not building strategy on bad data, please join us.

Can’t make it live? Register anyway, and we’ll send you the on-demand recording.

5 Ways Emerging Businesses Can Show up in ChatGPT, Gemini & Perplexity via @sejournal, @nofluffmktg

This post was sponsored by No Fluff. The opinions expressed in this article are the sponsor’s own.

When ChatGPT, Gemini, and Perplexity mention a company, these large language models (LLMs) are deciding whether that business is safe to reference, not how long it has existed.

Most business leaders assume one thing when they don’t show up in AI-generated answers:

We’re too new.

In reality, early testing across multiple AI platforms suggests something else is going on. In many cases, the problem has less to do with company age and more to do with how AI systems evaluate structure, repetition, and trust signals.

It is possible for new brands to be mentioned in AI search results.

Even well-built products with real expertise are routinely missing from AI recommendations. Yet when buyers ask who to trust, the same legacy names keep appearing.

Why Most New Businesses Don’t Show Up In AI Search Results

This isn’t random.

AI systems lean on existing training data and visible digital footprints, which favor brands that have been cited for years. Because every answer carries risk, these systems act conservatively.

They don’t look for the most optimized page; they look for the most verifiable entity. If your footprint is thin, inconsistent, or poorly supported by third parties, the AI will often swap you out for a competitor it can trust more easily.

Most new businesses launch with:

  • Minimal historical signals
    Very little online content or mentions, so AI has almost nothing to work with.
  • Few credibility signals
    Few backlinks, reviews, or press, so you don’t “look” trustworthy yet.
  • Blending brand names
    Similar or generic brand names are easier for AI systems to confuse, misattribute, or skip entirely if trust signals are weak.
  • Unclear positioning
    Unclear positioning or ideas that appear only once on a company website are less likely to be trusted.

Together, these create unreliable signals.

In generative search, visibility is less about ranking and more about reasoning.

This is why most new brands aren’t evaluated as “bad,” but as too uncertain to reference safely.

That distinction matters. Being referenced by AI is not just exposure; it influences who buyers consider credible before they ever reach a website. AI-referred visitors often convert at higher rates than traditional organic traffic.

For new businesses, the lack of legacy signals isn’t “just a disadvantage.” Handled correctly, it can be an opening to establish clarity and trust faster than older competitors that rely on outdated authority.

There’s surprisingly little guidance on whether a new or growing brand can actually appear in AI-generated answers. Given how much these systems depend on past signals, it’s easy to assume established companies appear by default.

To test that assumption, a brand-new B2B company was tracked from launch as part of a 12-week AI search visibility experiment. The findings below reflect the first six weeks of that ongoing test. The company started with no prior history, no backlinks, and no press coverage. A true zero.

Visibility was measured across 150 buyer-style prompts in ChatGPT, Google AI Overviews, and Perplexity rather than inferred from third-party dashboards.

Using weekly GEO sprints focused on technical foundations, answer-first content, and reinforcing signals like social, video, and early backlinks, the goal was to see how far a best-practice GEO playbook could move a truly new brand.

Within six weeks, the emerging business saw the following results:

  • Appeared in 5% of relevant AI responses.
  • Showed up across 39 of 150 questions.
  • Mentioned 74 times, with 42 cited mentions.
  • 6% citation accuracy, ~11% pointing to the brand’s own site.

6 Patterns Observed in Early AI Visibility Testing

Across the first six weeks, six patterns consistently influenced whether the brand was included, replaced by a competitor, or excluded entirely from AI-generated answers:

Pattern 1: Structure Matters More Than Topic

Image created by No Fluff, February 2026

Content that wandered (even if it was thoughtful or “robust”) consistently lagged in AI pickup. The pages that were picked up were tighter: they answered the question up front, broke the content into clear steps, and stuck to one idea at a time.

Pattern 2: The Social “Amplifier” Effect

AI is more likely to cite sources it already trusts. In the first two weeks, most citations came from the brand’s LinkedIn and Medium posts rather than its website. For a new brand, publishing key ideas first on high-authority platforms, including LinkedIn or Medium, often triggers AI pickup before the same content is indexed on your own website.

Image created by No Fluff, February 2026

Pattern 3: Hallucinations are Often Signal Failures

Image created by No Fluff, February 2026

When AI systems misidentify a new brand or confuse it with competitors, the cause is typically thin, slow, or conflicting signals. When pages failed to load within roughly 5–15 seconds, AI systems issue broader “fan-out” queries and assemble answers from adjacent or incorrect sources. Following improvements in site speed, crawl reliability, and entity clarity, the share of answers that correctly referenced this company’s own domain increased, while misattributed mentions declined.

Pattern 4: The 3-Week Indexing Window

The first AI pickup from a new domain can happen within three to four weeks. In this experiment, the first page was discovered on day 27. After that initial discovery, subsequent pages were picked up faster, with the shortest lag around eight days.

Image created by No Fluff, February 2026

Early inclusion wasn’t driven by content volume. It was driven by structure: a solid schema, consistent metadata, a clean, crawlable site, and machine-readable files such as llms.txt.

Pattern 5: Win the Explanatory Round First

New brands typically will not start by winning highly competitive, decision-stage prompts like “best” or “top” lists, unless the offering is truly unique or non-competitive. Before a brand can realistically be shortlisted, it must first be sourced as a primary authority for definitional or educational questions.

In the first 45 days, the goal wasn’t comparison visibility, but recognition and trust: getting AI systems to associate the brand with the right topics and sources. Early success is best measured by citation frequency, or how often a brand is used as the primary source for a given topic.

Pattern 6: Solve the Unfinished Trust Gap (Most Important)

Even with a well-structured site and strong content, brands struggle to get recommended without outside validation. The initial stages of this experiment showed AI answers defaulted to familiar domains and replaced newer brands with competitors that had clearer third-party mentions. This validates the importance of press and authoritative coverage early on. Waiting to “add it later” only slows trust.

5 Steps To Set A New Business Up For AI Visible Success

By now, the takeaway is clear: AI visibility doesn’t happen automatically once a site is live or a few campaigns are running. The good news is that this can be influenced deliberately. The steps below reflect the sequence that consistently moved a new brand from zero visibility to being cited in AI-generated answers. Rather than treating AI visibility as a side effect of SEO, this approach treats it as an operational problem: how to make a brand easy for AI systems to recognize, verify, and reuse.

Step 1: Map Your Brand Entity

Before building a site, you must define your brand in a way machines understand. ChatGPT, Gemini, and Perplexity don’t read your website the way humans do. They connect facts, names, and relationships into entities that define who you are. If those connections are missing or inconsistent, your brand simply won’t appear (no matter how much content you publish).

  • Define your business clearly using semantic triples: Use the [Subject] → [Predicate] → [Object] format (e.g., “Brand X” → “offers” → “Service Y”) to provide machine-readable facts.
  • Stick to public, widely understood language: Pull terminology from widely accepted sources like Wikipedia or Wikidata. If you describe your product using internal jargon that doesn’t match how the category is commonly defined, you risk being misclassified or overlooked.
  • State your authority: Define why your brand deserves trust. What facts, evidence, and proof back you up? Write 3–5 simple, factual claims you want to be known for.
  • Define your competitive counter-position: Be clear about what makes you different. Scope the specific niche you own (audience, problem, angle, or offering) that sets you apart from alternatives.

Step 2: Engineer Your Benchmark Prompt Set

You cannot rely on traditional SEO tools designed to track AI visibility. Most rely on inferred data or simulations, not on real prompts.

  • Map the competitive landscape: Identify which brands AI systems already reference, which buyer questions are realistically winnable, and where category language creates confusion.
  • Reverse-engineer buyer questions: Identify how buyers phrase real questions using keyword and competitor analysis (SEO tool data, People Also Ask, Google SERPS, and asking multiple AI engines themselves)
  • Lock your data set: Create a fixed set of 150 buyer-authentic questions across six clusters: Branded, Category, Problem, Comparison, and Advanced Semantic.
  • Start testing: Run these prompts weekly across ChatGPT, Gemini, and Perplexity to track your mentions and citation growth.

Step 3:  Make the Brand Machine-Readable

Make your site machine-readable to ensure AI bots don’t skip your content. AI systems don’t care about your website’s aesthetic; they care about how easily they can parse your data. If your technical signals are thin or conflicting, AI will hallucinate or substitute your brand with a competitor.

  • Implement JSON-LD Schema: Use Organization, Service, and FAQ schemas to tell AI exactly who you are and what you do.
  • Deploy an txt File: Place this at your domain root to provide a plain-text guide for AI crawlers, telling them how to describe your company and which pages to prioritize.
  • Eliminate crawling issues: Make sure your site is fully crawlable via robots.txt and that no content is hidden in gated PDFs or images. Most importantly, check site speed using PageSpeed Insights. Models don’t patiently wait for slow pages!

Step 4:  Publish “Retrieval-Ready” Content

Write for the impatient analyst (the AI bot). Start with high-leverage prompts, questions with real buyer intent that AI already answers, but only using a small and weak set of sources, making them easier to influence before trust fully locks in.

  • Lead with the answer: Start every section with a direct, factual answer.
  • Chunk semantically: Divide content into logical, independent sections that can be extracted and reused by AI without requiring the context of the entire page.
  • Consider the freshness factor: AI favors content updated within the last 60–90 days. For high-competition sectors like SaaS or Finance, content should be refreshed every three months to remain a “trusted” recommendation.

Step 5:  Earn External Validation

AI systems cross-check your site’s claims against the rest of the web.

  • Claim directory profiles: Align your entity data across Crunchbase, G2, LinkedIn, and Yelp. Inconsistencies across these profiles are a primary cause of AI hallucinations.
  • Target authoritative mentions: Secure mentions in industry-specific publications with consistent pickup throughout your prompts and or a strong domain rating.
  • External reinforcement: For every important page on your site, aim for at least three intentional external link-backs from authoritative sources to trigger AI pickup.

The Biggest Takeaway: Prioritize Authority as a Long-Term Game

For new brands, the limiting factor in AI search is not optimization. It’s authority.

AI systems are more likely to surface unfamiliar companies first in low-risk, explanatory answers, not in “best,” “top,” or comparison prompts. A clean site and solid SEO help a brand get recognized, but being recommended is a different hurdle.

In practice, early progress is about reducing uncertainty. When a brand consistently appears in third-party articles, reviews, or other independent sources, it becomes easier to explain and safer to reference. Without that outside validation, recommendations stall, no matter how strong the content or how fast the site loads.

This analysis covers the first phase of a live 90-day test examining how a new B2B brand earns visibility in AI-generated search results. Ongoing findings and final results will be published as the experiment concludes.


Image Credits

Featured Image: Image by No Fluff. Used with permission.

In-Post Images: Images by No Fluff. Used with permission.

The curious case of the disappearing Lamborghinis

When Sam Zahr first saw the gray Rolls-Royce Dawn convertible with orange interior and orange roof, he knew he’d found a perfect addition to his fleet. “It was very appealing to our clientele,” he told me. As the director of operations at Dream Luxury Rental, he outfits customers in the Detroit area looking to ride in style to a wedding, a graduation, or any other event with high-end vehicles—Rolls-Royces, Lamborghinis, Bentleys, Mercedes G-Wagons, and more.

But before he could rent out the Rolls, Zahr needed to get the car to Detroit from Miami, where he bought it from a used-car dealer. 

His team posted the convertible on Central Dispatch, an online marketplace that’s popular among car dealers, manufacturers, and owners who want to arrange vehicle shipments. It’s not too complicated, at least in theory: A typical listing includes the type of vehicle, zip codes of the origin and destination, dates for pickup and delivery, and the fee. Anyone with a Central Dispatch account can see the job, and an individual carrier or transport broker who wants it can call the number on the listing.

Zahr’s team got a call from a transport company that wanted the job. They agreed on the price and scheduled pickup for January 17, 2025. Zahr watched from a few feet away as the car was loaded into an enclosed trailer. He expected the vehicle to arrive in Detroit just a few days later—by January 21. 

But it never showed up.

Zahr called a contact at the transport company to ask what happened. 

“He’s like, I don’t know what you’re talking about.” 

Zahr told me his contact angrily told him they mostly ship Coca-Cola products, not luxury cars. “He was yelling and screaming about it,” Zahr said.

Over the years, people have broken into his business to steal cars, or they’ve rented them out and never come back. But until this day, he’d never had a car simply disappear during shipping. He’d expected no trouble this time around, especially since he’d used Central Dispatch—“a legit platform that everyone uses to transport cars,” he said. 

“That’s the scary part about it, you know?”

Wreaking havoc

Zahr had unwittingly been caught up in a new and growing type of organized criminal enterprise: vehicle transport fraud and theft. Crooks use email phishing, fraudulent paperwork, and other tactics to impersonate legitimate transport companies and get hired to deliver a luxury vehicle. They divert the shipment away from its intended destination and then use a mix of technology, computer skills, and old-school chop shop techniques to erase traces of the vehicle’s original ownership and registration.

These vehicles can be retitled and resold in the US or loaded into a shipping container and sent to an overseas buyer. In some cases, the car has been resold or is out of the country by the time the rightful owner even realizes it’s missing.

“Criminals have learned that stealing cars via the web portals has become extremely easy, and when I say easy—it’s become seamless,” says Steven Yariv, the CEO of Dealers Choice Auto Transport of West Palm Beach, Florida, one of the country’s largest luxury-vehicle transport brokers.

Individual cases have received media coverage thanks to the high value of the stolen cars and the fact that some belong to professional athletes and other celebrities. In late 2024, a Lamborghini Huracán belonging to Colorado Rockies third baseman Kris Bryant went missing en route to his home in Las Vegas; R&B singer Ray J told TMZ the same year that two Mercedes Maybachs never arrived in New York as planned; and last fall, NBA Hall of Famer Shaquille O’Neal had a $180,000 custom Range Rover stolen when the transport company hired to move the vehicle was hacked. “They’re saying they think it’s probably in Dubai by now, to be honest,” an employee of the company that customized the SUV told Shaq in a YouTube video.

“Criminals have learned that stealing cars via the web portals has become extremely easy, and when I say easy—it’s become seamless.”

Steven Yariv, CEO, Dealers Choice Auto Transport of West Palm Beach, Florida

But the nationwide epidemic of vehicle transport fraud and theft has remained under the radar, even as it’s rocked the industry over the past two years. MIT Technology Review identified more than a dozen cases involving high-end vehicles, obtained court records, and spoke to law enforcement, brokers, drivers, and victims in multiple states to reveal how transport fraud is wreaking havoc across the country.

RICHARD CHANCE

It’s challenging to quantify the scale of this type of crime, since there isn’t a single entity or association that tracks it. Still, these law enforcement officials and brokers, as well as the country’s biggest online car-transport marketplaces, acknowledge that fraud and theft are on the rise. 

When I spoke with him in August, Yariv estimated that around 8,000 exotic and high-end cars had been stolen since the spring of 2024, resulting in over $1 billion in losses. “You’re talking 30 cars a day [on] average is gone,” he said.

Multiple state and local law enforcement officials told MIT Technology Review that the number is plausible. (The FBI did not respond to a request for an interview.)

“It doesn’t surprise me,” said J.D. Decker, chief of the Nevada Department of Motor Vehicles’ police division and chair of the fraud subcommittee for the American Association of Motor Vehicle Administrators. “It’s a huge business.”

Data from the National Insurance Crime Bureau (NICB), a nonprofit that works with law enforcement and the insurance industry to investigate insurance fraud and related crimes, provides further evidence of this crime wave. NICB tracks both car theft and cargo theft, a broad category that refers to goods, money, or baggage that is stolen while part of a commercial shipment; the category also covers cases in which a vehicle is stolen via a diverted transport truck or a purloined car is loaded into a shipping container. NICB’s statistics about car theft show that it has declined following an increase during the pandemic—but over the same period cargo theft has dramatically increased, to an estimated $35 billion annually. The group projected in June that it was expected to rise 22% in 2025.

NICB doesn’t break out data for vehicles as opposed to other types of stolen cargo. But Bill Woolf, a regional director for the organization, said an antifraud initiative at the Port of Baltimore experienced a 200% increase from 2023 to 2024 in the number of stolen vehicles recovered. He said the jump could be due to the increased effort to identify stolen cars moving through the port, but he noted that earlier the day we spoke, agents had recovered two high-end stolen vehicles bound for overseas.

“One day, one container—a million dollars,” he said.

Many other vehicles are never recovered—perhaps a result of the speed with which they’re shipped off or sold. Travis Payne, an exotic-car dealer in Atlanta, told me that transport thieves often have buyers lined up before they take a car: “When they steal them, they have a plan.” 

In 2024, Payne spent months trying to locate a Rolls-Royce he’d purchased after it was stolen via transport fraud. It eventually turned up in the Instagram feed of a Mexican pop star, he says. He never got the car back.

The criminals are “gonna keep doing it,” he says, “because they make a couple phone calls, make a couple email accounts, and they get a $400,000 car for free. I mean, it makes them God, you know?”

Out-innovating the industry

The explosion of vehicle transport fraud follows a pattern that has played out across the economy over the past roughly two decades: A business that once ran on phones, faxes, and personal relationships shifted to online marketplaces that increased efficiency and brought down costs—but the reduction in human-to-human interaction introduced security vulnerabilities that allowed organized and often international fraudsters to enter the industry.

In the case of vehicle transport, the marketplaces are online “load boards” where car owners, dealerships, and manufacturers post about vehicles that need to be shipped from one location to another. Central Dispatch claims to be the largest vehicle load board and says on its website that thousands of vehicles are posted on its platform each day. It’s part of Cox Automotive, an industry juggernaut that owns major vehicle auctions, Autotrader, Kelley Blue Book, and other businesses that work with auto dealers, lenders, and buyers.

The system worked pretty well until roughly two years ago, when organized fraud rings began compromising broker and carrier accounts and exploiting loopholes in government licensing to steal loads with surprising ease and alarming frequency.

A theft can start with a phishing email that appears to come from a legitimate load board. The recipient, a broker or carrier, clicks a link in the message, which appears to go to the real site—but logging in sends the victim’s username and password to a criminal. The crook logs in as the victim, changes the account’s email and phone number to reroute all communications, and begins claiming loads of high-end vehicles. Cox Automotive declined an interview request but said in a statement that the “load board system still works well” and that “fraud impacts a very small portion” of listings.

“Every time we come up with a security measure to prevent the fraudster, they come up with a countermeasure.”

Bill Woolf, a regional director, National Insurance Crime Bureau

Criminals also gain access to online marketplaces by exploiting a lax regulatory environment. While a valid US Department of Transportation registration is required to access online marketplaces, it’s not hard for bad actors to register sham transport companies and obtain a USDOT number from the Federal Motor Carrier Safety Administration, the agency that regulates commercial motor vehicles. In other cases, criminals compromise the FMCSA accounts of legitimate companies and change their phone numbers and email addresses in order to impersonate them and steal loads. (USDOT did not respond to a request for comment.)

As Bek Abdullayev, the founder of Super Dispatch, one of Central Dispatch’s biggest competitors, explained in an episode of the podcast Auto Transport Co-Pilot, “FMCSA [is] authorizing people that are fraudulent companies—people that are not who they say they are.” He added that people can “game the system and … obtain paperwork that makes [them] look like a legitimate company.” For example, vehicle carrier insurance can be obtained quickly—if temporarily—by submitting an online application with fraudulent payment credentials.

The bottom line is that crooks have found myriad ways to present themselves as genuine and permitted vehicle transport brokers and carriers. Once hired to move a vehicle, they often repost the car on a load board using a different fraudulent or compromised account. While this kind of subcontracting, known as “double-­brokering,” is sometimes used by companies to save money, it can also be used by criminals to hire an unwitting accomplice to deliver the stolen car to their desired location. “They’re booking cars and then they’re just reposting them and dispatching them out to different routes,” says Yariv, the West Palm Beach transport broker. 

“A lot of this is cartel operated,” says Decker, of the Nevada DMV, who also serves on a vehicle fraud committee for the International Association of Chiefs of Police. “There’s so much money in it that it rivals selling drugs.”

Even though this problem is becoming increasingly well known, fraudsters continue to steal, largely with impunity. Brokers, auto industry insiders, and law enforcement told MIT Technology Review that load boards and the USDOT have been too slow to catch and ban bad actors. (In its statement, Cox Automotive said it has been “dedicated to continually enhancing our processes, technology, and education efforts across the industry to fight fraud.”)

Jake MacDonald, who leads Super Dispatch’s fraud monitoring and investigation efforts, put it bluntly on the podcast with Abdullayev: the reason that fraud is “jumping so much” is that “the industry is slowly moving over to a more technologically advanced position, but it’s so slow that fraud is actually [out-]innovating the industry.”

A Florida sting

As it turns out, the person Zahr’s team hired on Central Dispatch didn’t really work for the transport company. 

After securing the job, the fraudster reposted the orange-and-gray Rolls convertible to a load board. And instead of saying that the car needed to go from Miami to the real destination of Detroit, the new job listed an end point of Hallandale Beach, Florida, just 20 or so miles away. It was a classic case of malicious double-­brokering: the crooks claimed a load and then reposted it in order to find a new, unsuspecting driver to deliver the car into their possession.

On January 17 of last year, the legitimate driver showed up in a Dodge Ram and loaded the Rolls into an enclosed trailer as Zahr watched.

“The guy came in and looked very professional, and we took a video of him loading the car, taking pictures of everything,” Zahr told me. He never thought to double-­check where the driver was headed or which company he worked for.

Not long after a panicked Zahr spoke with his contact at the transport company he thought he was working with, he reported the car as stolen to the Miami police. Detective Ryan Chin was assigned to the case. It fit with a pattern of high-end auto theft that he and his colleagues had recently been tracking.

“Over the past few weeks, detectives have been made aware of a new method on the rise for vehicles being stolen by utilizing Central Dispatch,” Chin wrote in records obtained by MIT Technology Review. “Specific brokers are re-routing the truck drivers upon them picking up vehicles posted for transport and routing them to other locations provided by the broker.” 

Chin used Zahr’s photos and video to identify the truck and driver who’d taken the Rolls. By the time police found him, on January 31, the driver had already dropped off Zahr’s Rolls in Hallandale Beach. He’d also picked up and delivered a black Lamborghini Urus and a White Audi R8 for the same client. Each car had been stolen via double-brokering transport fraud, according to court records. 

The police department declined to comment or to make Chin available for an interview. But a source with knowledge of the case said the driver was “super cooperative.” (The source asked not to be identified because they were not authorized to speak to the media, and the driver does not appear to have been identified in court records.)

The driver told police that he had another load to pick up at a dealership in Naples, Florida, later that same day—a second Lamborghini Urus, this one orange. Police later discovered it was supposed to be shipped to California. But the carrier had been hired to bring the car, which retails for about $250,000, to a mall in nearby Aventura. He told police that he suspected it was going to be delivered to the same person who had booked him for the earlier Rolls, Audi, and Lamborghini deliveries, since “the voice sounds consistent with who [the driver] dealt with prior on the phone.” This drop-off was slated for 4 p.m. at the Waterways Shoppes mall in Aventura.

That was when Chin and a fellow detective, Orlando Rodriguez, decided to set up a sting. 

The officers and colleagues across three law enforcement agencies quickly positioned themselves in the Waterways parking lot ahead of the scheduled delivery of the Urus. They watched as, pretty much right on schedule that afternoon, the cooperative driver of the Dodge Ram rolled to a stop in the palm-tree-lined lot, which was surrounded by a kosher supermarket, Japanese and Middle Eastern restaurants, and a physiotherapy clinic.

The driver went inside the trailer and emerged in the orange Lamborghini. He parked it and waited near the vehicle.

Roughly 30 minutes later, a green Rolls-Royce Cullinan (price: $400,000 and up) arrived with two men and a teenager inside. They got out, opened the trunk, and sat on the tailgate of the vehicle as one man counted cash.

“They’re doing countersurveillance, looking around,” the source told me later. “It’s a little out of the ordinary, you know. They kept being fixated [on] where the truck was parked.” 

The transport driver and the three males who arrived in the Rolls-Royce did not interact. But soon enough, another luxury vehicle, a Bentley Continental GT, which last year retailed for about $250,000 and up, pulled in. The Bentley driver got out, took the cash from one of the men sitting on the back of the Rolls, and walked over to the transport driver. He handed him $700 and took the keys to the Lamborghini.

That’s when more than a dozen officers swooped in.

“They had nowhere to go,” the source told me. “We surrounded them.”

The two men in the Rolls were later identified as Arman Gevorgyan and Hrant Nazarian, and the man in the Bentley as Yuriy Korotovskyy. The three were arrested and charged with dealing in stolen property, grand theft over $100,000, and organized fraud. (The teenager who arrived in the Rolls was Gevorgyan’s son. He was detained and released, according to Richard Cooper, Gevorgyan’s attorney.)

As investigators dug into the case, the evidence suggested that this was part of the criminal pattern they’d been following. “I think it’s organized,” the source told me.

It’s something that transport industry insiders have talked about for a while, according to Fred Mills, the owner of Florida-based Advantage Auto Transport, a company that specializes in transporting high-end vehicles. He said there’s even a slang term to describe people engaged in transport fraud: the flip-flop mafia. 

It has multiple meanings. One is that the people who show up to transport or accept a vehicle “are out there wearing, you know, flip-flops and slides,” Mills says.

The second refers to how fraudsters “flip” from one carrier registration to another as they try to stay ahead of regulators and complaints.

In addition to needing a USDOT number, carriers working across states need an interstate operating authority (commonly known as an MC number) from the USDOT. Both IDs are typically printed on the driver and passenger doors. But the rise of ­double-brokering—and of fly-by-night and fraudulent carriers—means that drivers increasingly just tape IDs to their door. 

Mills says fraudsters will use a USDOT number for 10 or 11 months, racking up violations, and then tape up a new one. “They just wash, rinse, and repeat,” he says.

Decker from the Nevada DMV says a lot of high-end vehicles are stolen because dealerships and individual customers don’t properly check the paperwork or identity of the person who shows up to transport them.

“‘Flip-flop mafia’ is an apt nickname because it’s surprisingly easy to get a car on a truck and convince somebody that they’re a legitimate transport operation when they’re not,” he says.

Roughly a month after it disappeared, Zahr’s Rolls-Royce was recovered by the Miami Beach Police. Video footage obtained by a local TV station showed the gray car with its distinctive orange top being towed into a police garage. 

What happens in Vegas

Among the items confiscated from the men in Florida were $10,796 in cash and a GPS jammer. Law enforcement sources say jammers have become a core piece of technology for modern car thieves—necessary to disable the location tracking provided by GPS navigation systems in most cars. “Once they get the vehicles, they usually park them somewhere [and] put a signal jammer in there or cut out the GPS,” the Florida source told me. This buys them time to swap and reprogram the vehicle identification number (VIN), wipe car computers, and reprogram fobs to remove traces of the car’s provenance. 

No two VINs are the same, and each is assigned to a specific vehicle by the manufacturer. Where they’re placed inside a vehicle varies by make and model. The NICB’s Woolf says cars also have confidential VINs located in places—including their electronic components—that are supposed to be known only to law enforcement and his organization. But criminals have figured out how to find and change them.

“It’s making it more and more difficult for us to identify vehicles as stolen,” Woolf says. “Every time we come up with a security measure to prevent the fraudster, they come up with a countermeasure.”

All this doesn’t even take very much time. “If you know what you’re doing, and you steal the car at one o’clock today, you can have it completely done at two o’clock today,” says Woolf. A vehicle can be rerouted, reprogrammed, re-VINed, and sometimes even retitled before an owner files a police report.

That appears to have been the plan in the case of the stolen light-gray 2023 Lamborghini Huracán owned by the Rockies’ Kris Bryant.

On September 29, 2024, a carrier hired via a load board arrived at Bryant’s home in Cherry Hills, Colorado, to pick up the car. It was supposed to be transported to Bryant’s Las Vegas residence within a few days. It never showed up there—but it was in fact in Vegas.

Using Flock traffic cameras, which capture license plate information in areas across the country, Detective Justin Smith of the Cherry Hills Village Police Department tracked the truck and trailer that had picked up the Lambo to Nevada, and he alerted local police.

On October 7, a Las Vegas officer spotted a car matching the Lamborghini’s description and pulled it over. The driver said the Huracán had been brought to his auto shop by a man whom the police were able to identify as Dat Viet Tieu. They arrested Tieu later that same day. In an interview with police, he identified himself as a car broker. He said he was going to resell the Lamborghini and that he had no idea that the car was stolen, according to the arrest report. 

Police searched a Jeep Wrangler that Tieu had parked nearby and discovered it had been stolen—and had been re-VINed, retitled, and registered to his wife. Inside the car, police discovered “multiple fraudulent VIN stickers, key fobs to other high-end stolen vehicles, and fictitious placards,” their report said. 

One of the fake VINs matched the make and model of Bryant’s Lamborghini. (Representatives for Bryant and the Rockies did not respond to a request for comment.) 

Tieu was released on bail. But after he returned to LVPD headquarters two days later, on October 9, to reclaim his personal property, officers secretly placed him under surveillance with the hope that he’d lead them to one of the other stolen cars matching the key fobs they’d found in the Jeep. 

It didn’t take long for them to get lucky. A few hours after leaving the police station, Tieu drove to Harry Reid International Airport, where he picked up an unidentified man. They drove to the Caesars Palace parking garage and pulled in near a GMC Sierra. Over the next three hours, the man worked on a laptop inside and outside the vehicle, according to a police report. At one point, he and Tieu connected jumper cables from Tieu’s rented Toyota Camry to the Sierra.

“At 2323 hours, the white male adult enters the GMC Sierra, and the vehicle’s ignition starts. It was readily apparent the [two men] had successfully re-programmed a key fob to the GMC Sierra,” the report said.

An officer watched as the man gave two key fobs to Tieu, who handed the man an unknown amount of cash. Still, the police let the men leave the garage. 

The police kept Tieu and his wife under surveillance for more than a week. Then, on October 18, fearing the couple was about to leave town, officers entered Nora’s Italian Restaurant just off the Vegas Strip and took them into custody.

“Obviously, we meet again,” a detective told Tieu.

“I’m not surprised,” Tieu replied. 

Police later searched the VIN on the Sierra from the Caesars lot and found that it had been reported stolen in Tremonton, Utah, roughly two weeks earlier. They eventually returned both the Sierra and Kris Bryant’s Lamborghini to their owners. 

Tieu pleaded guilty to two felony counts of possession of a stolen vehicle and one count of defacing, altering, substituting, or removing a VIN. In October, he was sentenced to up to one year of probation; if it’s completed successfully, the plea agreement says, the counts of possession of a stolen vehicle will be dismissed. His attorneys, David Z. Chesnoff and Richard A. Schonfeld, said in a statement that they were “pleased” with the court’s decision, “in light of [Tieu’s] acceptance of responsibility.” 

Taking the heat

Many vehicles stolen via transport fraud are never recovered. Experts say the best way to stop this criminal cycle would be to disrupt it before it starts. 

That would require significant changes to the way that load boards operate. Bryant’s Lamborghini, Zahr’s and Payne’s Rolls-Royces, and the orange Lamborghini Urus in Florida were all posted for transport on Central Dispatch. Both brokers and shippers argue that the company hasn’t taken enough responsibility for what they characterize as weak oversight.

“If the crap hits the fan, it’s on us as a broker, or it’s on the trucking company … they have no liability in the whole transaction process. So it definitely frosted a lot of people’s feathers.”

Fred Mills, owner of Florida-based Advantage Auto Transport

“You’re Cox Automotive—you’re the biggest car company in the world for dealers—and you’re not doing better screenings when you sign people up?” says Payne. (The spokesperson for Cox Automotive said that it has “a robust verification process for all clients … who sign up.”)

“If the crap hits the fan, it’s on us as a broker, or it’s on the trucking company, or the clients’ insurance, [which means] that they have no liability in the whole transaction process,” says Mills. “So it definitely frosted a lot of people’s feathers.”

Over the last year, Central Dispatch has made changes to further secure its platform. It introduced two-factor authentication for user accounts and started enabling shippers to use its app to track loads in real time, among other measures. It also kicked off an awareness campaign that includes online educational content and media appearances to communicate that the company takes its responsibilities seriously.

“We’ve removed over 500 accounts already in 2025, and we’ll continue to take any of that aggressive action where it’s needed,” said Lainey Sibble, Central Dispatch’s head of business, in a sponsored episode of the Auto Remarketing Podcast. “We also recognize this is not going to happen in a silo. Everyone has a role to play here, and it’s really going to take us all working together in partnership to combat this issue.”

Mills says Central Dispatch got faster at shutting down fraudulent accounts toward the end of last year. But it’s going to take time to fix the industry, he adds: “I compare it to a 15-year opioid addiction. It’s going to take a while to detox the system.” 

Yariv, the broker in West Palm Beach, says he has stopped using Central Dispatch and other load boards altogether. “One person has access here, and that’s me. I don’t even log in,” he told me. His team has gone back to working the phones, as evidenced by the din of voices in the background as we spoke. 

RICHARD CHANCE

“[The fraud is] everywhere. It’s constant,” he said. “The only way it goes away is the dispatch boards have to be shut down—and that’ll never happen.”

It also remains to be seen what kind of accountability there will be for the alleged thieves in Florida. Korotovskyy and Nazarian pleaded not guilty; as of press time, their trials were scheduled to begin in May. (Korotovskyy’s lawyer, Bruce Prober, said in a statement that the case “is an ongoing matter” and his client is “presumed innocent,” while Nazarian’s attorney, Yale Sanford, said in a statement, “As the investigation continues, Mr. Nazarian firmly asserts his innocence.” A spokesperson with Florida’s Office of the State Attorney emailed a statement: “The circumstances related to these arrests are still a matter of investigation and prosecution. It would be inappropriate to be commenting further.”)

In contrast, Gevorgyan, the third man arrested in the Florida sting, pleaded guilty to four charges. 

Yet he maintains his innocence, according to Cooper, his lawyer: “He was pleading [guilty] to get out and go home.” Cooper describes his client as a wealthy Armenian national who runs a jewelry business back home, adding that he was deported to Armenia in September. 

Cooper says his client’s “sweetheart” plea deal doesn’t require him to testify or otherwise supply information against his alleged co-conspirators—or to reveal details about how all these luxury cars were mysteriously disappearing across South Florida. Cooper also says prosecutors may have a difficult time convicting the other two men, arguing that police acted prematurely by arresting the trio without first seeing what, if anything, they intended to do with the Lamborghini.

“All they ever had,” Cooper says, “was three schmucks sitting outside of the Lamborghini.” 


Craig Silverman is an award-winning journalist and the cofounder of Indicator, a publication that reports on digital deception.

The Download: the rise of luxury car theft, and fighting antimicrobial resistance

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.

The curious case of the disappearing Lamborghinis

Across the world, unsuspecting people are unwittingly becoming caught up in a new and growing type of organized criminal enterprise: vehicle transport fraud and theft.

Crooks use email phishing, fraudulent paperwork, and other tactics to impersonate legitimate transport companies and get hired to deliver a luxury vehicle. They divert the shipment away from its intended destination before using a mix of technology, computer skills, and old-school techniques to erase traces of the vehicle’s original ownership and registration. In some cases, the car has been resold or is out of the country by the time the rightful owner even realizes it’s missing.

The nationwide epidemic of vehicle transport fraud and theft has remained under the radar, even as it’s rocked the industry over the past two years. MIT Technology Review identified more than a dozen cases involving high-end vehicles, obtained court records, and spoke to law enforcement, brokers, drivers, and victims in multiple states to reveal how transport fraud is wreaking havoc across the country. Read the full story.

—Craig Silverman

The scientist using AI to hunt for antibiotics just about everywhere

Antimicrobial resistance is a major problem. Infections caused by bacteria, fungi, and viruses that have evolved ways to evade treatments are now associated with more than 4 million deaths per year, and a recent analysis predicts that number could surge past 8 million by 2050.

Bioengineer and computational biologist César de la Fuente has a plan. His team at the University of Pennsylvania is training AI tools to search genomes far and deep for peptides with antibiotic properties. His vision is to assemble those peptides—molecules made of up to 50 amino acids linked together—into various configurations, including some never seen in nature. The results, he hopes, could defend the body against microbes that withstand traditional treatments—and his quest has unearthed promising candidates in unexpected places. Read the full story.

—Stephen Ornes

These stories are both from the next print issue of MIT Technology Review magazine, which is all about crime. If you haven’t already, subscribe now to receive future issues once they land. 

The must-reads

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

1 The Pentagon is close to cutting all business ties with Anthropic
The move would force anyone who wants to deal with the US military to cease working with Anthropic too. (Axios)
+ Claude was used in the US raid to capture the former Venezuelan President. (WSJ $)
+ Generative AI is learning to spy for the US military.
(MIT Technology Review)

2 RFK Jr is setting his sights on baby formula
But advocacy groups are concerned about how grounded in science the administration’s overhaul suggestions will be. (WSJ $)

3 Germany is edging closer to banning social media for under-16s
In an effort to create safer digital spaces for young web users. (Bloomberg $)
+ The country’s centre-left is in agreement with their conservative coalition partners. (Reuters)

4 Creative hackers are fighting back against ICE
The maker community is resisting through laser-cutting and 3D-printing. (Wired $)
+ ICE has signed hundreds of deals with local law enforcement. (NBC News)

5 Consultancies have built thousands of AI agents
Now it’s time to see if they can actually deliver. (Insider $)
+ Don’t let hype about AI agents get ahead of reality. (MIT Technology Review)

6 Restaurant workers are sick of being recorded 👓
Meta’s smart glasses make the video-recording process more surreptitious than ever. (NYT $)

7 The Arctic’s rivers are turning bright orange
But it’s climate change, not mining, that’s to blame. (FT $)
+ What’s going to happen now the EPA can no longer fight climate change? (Undark)
+ Scientists can see Earth’s permafrost thawing from space. (MIT Technology Review)

8 NASA let AI drive its Mars Perseverance rover
It traversed 456 meters across two days without human intervention. (IEEE Spectrum)
+ That’s…not very fast at all. (Semafor)
+ Slow-moving food delivery robots are under attack in the US. (Economist $)

9 This machine is able to translate photos into smells
Select your images very carefully, is my advice.(Fast Company $)

10 One of YouTube’s biggest creators is now a successful director 
Mark Fischbach funded, made and released his film in theaters entirely independently. (The Atlantic $)

Quote of the day

“My advice to them would be to get with the program.”

—Jeremy Newmark, leader of a British council near the town of Potters Bar, has some choice words for the locals disputing plans to build a massive AI data center nearby, Wired reports.

One more thing

The quest to find out how our bodies react to extreme temperatures

Climate change is subjecting vulnerable people to temperatures that push their limits. In 2023, about 47,000 heat-related deaths are believed to have occurred in Europe. Researchers estimate that climate change could add an extra 2.3 million European heat deaths this century. That’s heightened the stakes for solving the mystery of just what happens to bodies in extreme conditions.

While we broadly know how people thermoregulate, the science of keeping warm or cool is mottled with blind spots. Researchers around the world are revising rules about when extremes veer from uncomfortable to deadly. Their findings change how we should think about the limits of hot and cold—and how to survive in a new world. Read the full story.

—Max G.Levy

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Low-Value Imports Upend E.U. Ecommerce

The demand by E.U. consumers for low-value imports is reshaping the region’s online retailing.

In 2025, about 5.9 billion parcels valued at €150 or less (roughly $177) entered the E.U., continuing a multi-year surge that saw such shipments rise from 1.4 billion in 2022. At approximately one parcel per E.U. citizen per month, the scale reflects not just rapid growth but widespread consumer engagement.

The European Commission estimates that, in 2025, low-value goods accounted for less than 5% of the overall value of imports but 98% of the volume. Depending on the source, roughly one-third of 2025 ecommerce revenue in the E.U. came from imported products.

Still, low-priced goods now arrive in massive quantities, with slow delivery times that shoppers accept. Customs authorities struggle to process the flow, and policymakers describe the system as unsustainable.

The effect reshapes the competitive environment for all merchants operating in the E.U., domestic and foreign.

Even if parcel volumes stabilize or decline under regulatory pressure, consumer price norms rarely revert.

Price Expectations

U.S. merchants often underestimate the impact of product categories dominated by items priced under €10. They assume competition comes mainly from local European brands. In practice, they face a global supply chain that has normalized ultra-low prices and conditioned buyers to accept slower delivery.

Hence expansion into the E.U. now carries elevated risk for specific categories, including:

  • Commoditized products,
  • Mid-priced goods without visible differentiation,
  • Products where quality, safety, or after-sales support is unclear,
  • Offers driven mainly by branding rather than features.

Despite these pressures, the E.U. remains one of the world’s largest ecommerce markets. Opportunities remain in segments where price is not the primary factor, including:

  • Products with clear functional or technical differentiation,
  • Categories where safety, durability, or compliance matters,
  • Premium or specialist goods supported by service and logistics,
  • Offers backed by guarantees, support, and transparent returns.

Risks

E.U. policymakers are responding to the surge in low-value parcels. Measures include removing the €150 customs duty exemption, imposing a €3 flat customs fee on such parcels, and shifting responsibility to marketplaces deemed importers.

The E.U. ecommerce market remains attractive for merchants with strong positioning and operational discipline.

The real risk for U.S. and other foreign merchants is entering the E.U. with an outdated view of the market. Success now requires goods clearly superior to €10 alternatives.

4 Pillars To Turn Your “Sticky-Taped” Tech Stack Into a Modern Publishing Engine

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

In the race for audience attention, digital marketers at media companies often have one hand tied behind their backs. The mission is clear: drive sustainable revenue, increase engagement, and stay ahead of technological disruptions such as LLMs and AI agents.

Yet, for many media organizations, execution is throttled by a “Sticky-taped stack,” which is a fragile, patchwork legacy CMS structure and ad-hoc plugins. For a digital marketing leader, this isn’t just a technical headache; it’s a direct hit to the bottom line.

It’s time to examine the Fragmentation Tax, and why a new publishing standard is required to reclaim growth.

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.

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.

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.

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Featured Image: Image by WP Engine. Used with permission.

In-Post Images: Image by WP Engine. Used with permission.