I’ve always found SEO reporting to be a bit of a hassle. It used to mean spending hours exporting data from Google Search Console (GSC), tidying it up in spreadsheets, and then trying to make sense of it all in Data Studio.
Now, with AI tools like Claude Code, my workflow has completely changed. I can instantly create customized data visuals and reports in a fraction of the time it used to take.
Let me walk you through the journey of transforming GSC data into tailored reports, streamlining the entire process.
Using Claude Code is different from the standard Claude experience. While the regular Claude.ai acts like a chatbot, Claude Code functions as an AI coding assistant right on my computer. It’s capable of reading GSC CSV files, analyzing large datasets, and transforming raw data into clear, visual reports.
Initially, setting up Claude Code can be daunting, especially if you aren’t familiar with technical tasks. But don’t worry, the setup is a one-time effort. Once it’s up and running, generating reports takes just minutes.
The real magic happens after you connect Claude to GSC. Whether you’re in an enterprise environment or you’re an independent SEO consultant, having Claude Code set up is invaluable.
Starting your journey with Claude Code begins by creating an account on Claude.ai. Even without a paid subscription, I find the platform extremely helpful for generating SEO reports.
A crucial step in using Claude Code is installing Node.js on your machine. For this tutorial, I used a Mac, but it’s compatible with other operating systems too. Once Node.js is installed, I am able to install Claude Code and verify my setup through simple terminal commands.
After setting everything up, I navigated a series of prompts in Claude, choosing how to access GSC data and defining key parameters for my reporting.
Connecting Claude to GSC involves interacting with the Search Console API, albeit a bit technical. But Claude guides me through each step, ensuring a smooth setup.
The exciting part comes after the connection is established. I can now rapidly create focused reports, such as identifying top-performing pages or tracking keyword trends over time, tailor-made for my needs.
Overall, Claude Code redefines how I manage SEO reporting. It offers the perfect balance of speed, flexibility, and control. Once the groundwork is laid, it makes my reporting both dynamic and precise, adapting to the demands of my stakeholders with ease.
As I dive into the ever-evolving world of marketing, I can’t help but notice a profound shift. We’re no longer just performing for an audience; we’re adapting to customer journeys that mirror advanced AI systems. These systems interpret trust, risk, intent, and identity in real-time, and it feels like a whole new era.
For much of marketing’s history, the game plan was almost theatrical. Brands performed while consumers watched, and marketing channels existed primarily to broadcast these performances efficiently. Even as performance marketing gained popularity, it was still fundamentally based on the idea that a real person was sitting on the other side of the screen making straightforward decisions.
But now, that model is shattering. It’s not that consumers have disappeared; it’s that software is now an integral part of decision-making, demanding marketers’ attention.
Recommendation engines, fraud models, identity systems, and inbox providers have taken the reins more forcefully than creative campaigns ever did. Algorithms are shaping where attention goes long before consumers consciously choose anything.
I find myself contemplating the implications of layering autonomous agents into this complex environment. We often talk about AI as if it’s just another tool to enhance productivity—helping us segment faster, generate content quicker, and optimize swifter. This framing is comforting because it implies humans are still the pilots, with AI acting as copilots.
But this perspective will likely become outdated.
We are witnessing the rise of machine coordination. What is unfolding is less about workflow automation and more about distributed machine coordination. Here, marketing becomes an orchestration layer, interacting with thousands of semi-independent systems that interpret intent, trust, risk, relevance, identity, and value simultaneously.
Marketing is beginning to resemble air traffic control more than broadcasting.
Marketers aren’t gaining more control; they’re becoming like air traffic controllers. We govern dynamic systems we can’t fully see, predict, or command. Our value lies in maintaining harmony under challenging conditions of limited visibility and escalating complexity.
Today’s customer journey feels like a negotiation between competing models. One predicts purchase intent, while another assesses fraud risk or alters outreach frequency. These competing systems aren’t sequential but simultaneous, often adversarial.
Many organizations are already embroiled in this machine ecosystem, making contradictory decisions about customers simultaneously. One system may label a user as high value while another suppresses them as suspicious.
AI merely speeds up the revelation of these inconsistencies.
This scenario partly explains why identity infrastructure is moving back to the forefront. Over years spent focusing on activation, we’ve neglected signal integrity. This was manageable when humans were dominant interpreters. But autonomous systems operationalize ambiguity instead of compensating for it.
Having an inaccurate identity layer in a partially automated environment resembles corrupted air traffic telemetry. Small inconsistencies compound, leading to multiplied routing errors and deteriorating trust.
For marketing leaders, creativity is more important than ever, but at an architectural rather than asset level. The strategic advantage might lie with those who design stable coordination systems between machine intelligence layers.
This shift changes the strategic role of signal networks, once seen as supporting functions, to central components of a successful marketing strategy.
In this landscape driven by autonomous decision-making, orchestration quality is inseparable from identity confidence quality. If systems can’t differentiate between signal and noise or real activity and mimicry, they can’t coordinate effectively.
Companies might soon realize they can’t discern how much of their performance is actual human value versus synthetic behavior. AI systems optimize for measurable success rather than truth, occasionally rewarding synthetic engagement until financial or legal consequences arise.
This evolving environment makes personalization less about predicting customer desires and more about maintaining stable trust frameworks across intricate systems of human, AI, and synthetic interactions.
Today’s competitive advantage hinges on creating resilient signal infrastructures rather than stockpiling data. More information doesn’t always yield clarity and can sometimes create interference instead.
Activity-based intelligence is becoming crucial beyond traditional campaign optimization. Identity confidence and cross-channel trust are now vital components of autonomous ecosystems.
The shift favors organizations maintaining operational trust while scaling automation, moving away from systems built on static assumptions to those grounded in ongoing real-world activity.
This juxtaposition reveals the irony of years-long advice for marketing teams to become more scientific and data-driven. Scaling intelligence without scaling signal integrity equates to advancing aircraft technology while ignoring radar calibration.
Visibility, rather than data abundance, is about to become the defining constraint.
But not just visibility into consumers—visibility into the systems acting on their behalf.
AI can elevate SEO and GEO audits dramatically, but only if you equip it with the right data, methodology, and human oversight.
As someone deeply involved in the world of B2B tech SEO, I find it fascinating how AI is reshaping our strategies. However, I’ve noticed a trend among clients who provide AI-generated audits—what I term ‘naive audits.’ While these reports often appear detailed, they miss crucial components. When I inquire about their basis, data sources, or methodology, they frequently crumble under scrutiny.
This gap between expectation and delivery inspired me to propose a simple framework focusing on three critical elements—context, methodology, and human oversight—to ensure AI-driven audits provide genuine value.
Imagine asking an advanced language model, like Claude or ChatGPT, to perform a simple SEO task, such as optimizing a blog post. The result? A 1,600-word detailed analysis filled with assumptions and errors, due to lack of access to the full content or appropriate keywords. Sounds familiar, right?
Despite the capabilities of models like Claude, I discovered severe limitations. For instance, it couldn’t read the original article, basing its recommendations on search snippets instead. Not only was the suggested keyword, ‘intelligent data tiering,’ void of search volume, but the analysis itself was flawed as well.
Ensuring an audit is grounded in reality requires agents that are self-sufficient and well-informed. They must include an understanding of content, an appropriate methodology, and concise, actionable recommendations. I believe in empowering busy writers by offering bite-sized guidance rather than overwhelming them with lengthy reports.
When building a page audit agent, I follow these essential steps: pre-scraping webpage content, leveraging keyword tools, accessing top URLs for key queries, and aligning recommendations with structured content outlines—all while maintaining a human in the loop to ensure accuracy and practicality.
So, when asking AI to execute GEO/AEO audits, one must be cautious of potential pitfalls. The knowledge base for AI in these emerging fields is riddled with speculative insights and inconsistent data. That’s why partnering with experts actively engaged in experimentation remains invaluable.
Ultimately, my CaML framework—short for Context, Methodology, and Human in the Loop—ensures that AI audits are comprehensive and substantial. Just as a camel is equipped to withstand the harsh desert environment, a well-prepared AI agent should be resilient to the challenges of digital landscapes.
Envision a future where SEO roles are redefined, focusing on strategic guidance and unique insights rather than laborious manual tasks. Our agency’s transition to an agent-first model embodies this shift, and I’m excited to be on this transformative journey.
I’ve always believed in the power of strong SEO strategies, especially when it comes to law firms. While technical SEO and content are crucial, I’ve learned that lasting success relies heavily on building authority across the web.
Most law firms, including my own, start by heavily investing in content and refining technical foundations. Initially, these efforts pay off, but eventually, we hit a wall — results plateau, and the instinct is to do more of the same. But I’ve realized that’s not enough.
For me, the challenge isn’t about the effort or execution. It’s about addressing the missing link: authority. Without genuine, verifiable credibility, any progress made quickly stalls, especially in an AI-driven search landscape that constantly evolves.
Authority isn’t about just churning out content for the sake of it. It’s about being recognized as a trusted, expert source beyond our own website. This includes getting cited, mentioned, and connected with reputable publications and platforms relevant to our field.
I’ve come to see how critical the E-E-A-T framework is in building authority. It helps to assess whether my firm deserves its ranking positions. This means showcasing attorneys’ credentials, ensuring content reflects real expertise, and maintaining a consistent online presence across various platforms.
The dynamic nature of AI in search underscores the importance of authority even more. AI doesn’t just rely on optimized pages; it looks for credible sources. This means new layers of opportunity and competition for law firms like mine.
To build authority effectively, I’ve focused on auditing our online footprint, understanding where we stand, and identifying gaps in our visibility. We’ve shifted our content strategy to prioritize citable content over merely indexable material.
I’ve realized that authority grows over time and requires consistency across various platforms. Engaging in meaningful digital PR and forming connections within the legal community are crucial to developing a strong, cohesive digital identity.
The key takeaway for anyone in my position is clear: building authority isn’t a quick fix. It’s an ongoing effort that requires looking beyond traditional SEO to embrace a holistic approach to digital presence.
I’ve recently delved into Google’s exciting release of Ads API version 24.1, and it’s packed with valuable updates for advertisers. This version brings us advanced reporting capabilities, expanded AI campaign testing, and improved security measures.
In this update, Google has prepared us for their upcoming data retention policy changes, which will commence next year—something I believe every developer should be ready for.
Why we care. The latest release highlights three crucial areas: performance visibility, creative control, and testing automation, which are becoming vital for advertisers like me.
What’s more, brands now have greater control over creative displays in Demand Gen campaigns, overcoming the typical limits imposed by automation. It’s a significant update that I’m excited to explore further.
Those of us who lean heavily on reporting infrastructures should also be mindful of Google’s impending 37-month data retention limit, set to impact historical performance analysis come 2026.
Mobile reporting gets more granular. One of the features I’m most thrilled about is the new mobile device platform segment that allows for reporting by operating system.
With the new segments.mobile_device_platform field, I’m able to differentiate performance across iOS and Android, a game-changer for app marketers and ecommerce advertisers alike.
Demand Gen adds classic image support. I love how Google is providing us with more creative control in Demand Gen campaigns, specifically through the classic_display_images field.
This new field allows us to upload and display static image ads exactly as designed, which is perfect for maintaining branding consistency without AI alterations.
Passkeys come to Google Ads. Security is always a top concern of mine, so I’m pleased to see the inclusion of the passkey_enabled field to boost account security through passwordless authentication.
Experiment support expands. I’ve noticed that Google has significantly enhanced the support for Experiments, allowing us to run and analyze tests across AI Max, Video, Demand Gen, and Performance Max campaigns.
This update also enables us to view metrics such as clicks and conversions more transparently, making experiment analysis straightforward and insightful.
A major data retention change is coming. From June 1st, Google Ads and related APIs will enforce a 37-month data retention limit, something I must prepare for to avoid disruptions in performance analytics.
The release includes a new error code: DateRangeError.REQUESTED_DATE_GRANULARITY_NOT_SUPPORTED, and it’s essential that I update reporting workflows accordingly.
What’s next. I’ve already checked out the updated client libraries and code samples for v24.1, and I plan to participate in Google’s live walkthrough on Discord, YouTube Live, and LinkedIn Live for additional insights.
I’ve discovered that LinkedIn is more than just a networking platform—it’s a powerhouse for B2B discovery, especially with its growing influence on AI search results.
Recently, LinkedIn has emerged as a prime resource for how B2B buyers use AI to find products and services. By optimizing our LinkedIn profiles and content for AI search, I noticed a significant boost in our brand’s visibility.
Through my work with B2B clients, especially those in high-growth SaaS sectors, I’ve categorized our LinkedIn optimization into three main strategies:
Optimize earned media.
Feed LLMs strategic content.
Invest in post-engagement that strengthens LLM signals.
Here’s my approach to each area and the results you can expect.
1. Optimize Earned Media: Websites and LinkedIn Pages
Keeping our website and LinkedIn pages up to date is crucial. These include our company page and profiles of high-profile employees, like thought leaders who contribute content. This optimization signals to LLMs that we are a credible source of information.
Google’s E-E-A-T principles are parallel to how LLMs evaluate our media. Content published by our brand’s reps can enhance our credibility when it’s well-optimized.
On Websites
Ensure the business address, contact details, and product descriptions on your site are accurate and comprehensive.
On LinkedIn Company Pages
Regularly update the “About” section and services you offer. Reflect industry specifics where applicable to align with LLM prompts.
Consider the profiles of executives and thought leaders as brand extensions. Their active engagement and representation of the company further reinforce our authenticity to LLMs.
2. Feed the LLMs Strategic Content
Long-form content, specifically between 800-1,200 words, has shown to be more beneficial for AEO mentions. On LinkedIn, users anticipate in-depth content in articles and newsletters, making them perfect vehicles for these insights.
While engagement through carousels and videos is valuable, well-crafted written content seems to be highly favored by LLMs.
3. Invest in Building Post Engagement
LinkedIn posts that attract significant engagement—at least 10 quality comments or 60 reactions—are highly regarded by LLMs due to the social proof they offer. This engagement level doesn’t necessarily require a large budget increase.
Boosting company posts and utilizing Thought Leader Ads (TLAs) and follower ads can further bolster engagement and brand reach. Engaging content on employee profiles, particularly those with fewer than 3,000 followers, is seen as more trustworthy.
Empowering employees and forming partnerships with industry experts can amplify your content reach and reinforce your brand authority.
AI Search is Expanding LinkedIn’s Influence in B2B
Every B2B marketer should prioritize AEO in their strategy. The influence of AI search continues to grow, and staying ahead with LinkedIn optimization is key to capturing new opportunities.
SEO isn’t dead—far from it. But let’s face it, AI is definitely changing the game in ways we never imagined. This got me thinking about how things are looking different for us, especially with the rise of zero-click searches and AI Overviews. In 2026, these are becoming more like the hand guiding our SEO strategies.
With AI advancements, I’m seeing how crucial it is for all of us to adapt and build our SEO approaches around these innovations. Answer Engine Optimization (AEO) is making waves, and it’s fascinating to watch how it reshapes our tactics.
If we want to stay ahead, integrating AI into our SEO strategies isn’t just optional—it’s essential. The landscape is evolving, and so should we.
As I delve into the world of artificial intelligence, I’ve been stunned by the numerous legal risks that businesses face, including those related to copyright, privacy, misinformation, and compliance. While AI is still growing, the risks are growing rapidly with it.
The legal landscape is changing, especially with Europe leading the charge through the EU Artificial Intelligence Act. In the US, almost 20 states have enacted AI-related legislation. Yet, the federal government’s stance on keeping regulations light is evident in the AI policy wishlist from the White House.
Despite the pace at which new regulations appear, AI isn’t reshaping the legal landscape; it’s accelerating it. Risks often trace back to known legal domains such as intellectual property, privacy, consumer protection, and liability.
So rather than considering ‘AI law’ as something entirely novel, it’s more beneficial for me to identify where familiar legal risks stem from within business operations.
I learned that AI risks are prominently apparent in nine business areas. Addressing them doesn’t require legal expertise, just keen questioning to address each concern effectively.
Let me walk you through these areas:
1. Intellectual Property The key question here is: Who owns the work, and are we unknowingly using someone else’s intellectual property?
In AI, ownership is still being defined. However, the U.S. Copyright Office indicates that works purely generated by AI are not protected. Human creativity must play a significant role in shaping AI’s outputs for potential protection.
Using patented ideas conceived by humans but developed with AI remains in question as per the U.S. Patent and Trademark Office’s revised guidelines. These questions aren’t theoretical; they highlight real, current challenges organizations face.
Emerging case filings, such as The New York Times lawsuit against OpenAI, showcase the ever-growing concern over infringement risks.
Two primary risks stand out: unintentional incorporation of protected material in AI outputs and proving ownership without sufficient human creativity involved. In content creation, human involvement isn’t a luxury; it’s an absolute necessity.
2. Advertising and Misinformation The pivotal question I consider is: What message are we crafting, and is it accurate?
AI tools empower us to create vast amounts of content, which is advantageous. However, the risk of distributing misleading or incorrect information exists. I witnessed Google Bard’s numerous errors during a product demo, which negatively impacted its market value by $100 billion.
The emergence of hallucinated data, fabricated citations, and flawed reasoning are challenges businesses face when publishing under their brand. I understand that a single error can severely damage reputation.
3. Privacy and Personal Data The question guiding me is: Are we handling people’s data lawfully, transparently, and respectfully?
Consumer expectations on data privacy have significantly shifted. Legal frameworks like the EU’s GDPR, Canada’s PIPEDA, and California’s CCPA set new standards for collecting, using, and disclosing personal data.
We’ve seen how regulators treat these matters seriously; Italy blocked ChatGPT over privacy issues. Clear policies on data handling are crucial for any organization, and swift communication is required when a customer inquires under prevailing laws.
As I continue exploring AI’s implications on business, these areas underscore the necessity of thoughtful and deliberate strategies to manage AI’s legal implications effectively.
I recently discovered how AI is revolutionizing the way customers find local businesses. Tools like Google AI Overviews, Gemini, and Ask Maps are paving the way for more detailed, conversational searches.
It’s clear to me that traditional search rankings are no longer the sole factor in gaining visibility. Ensuring your business details are complete and accurate—like your Google Business Profile, reviews, and local content—can make a big difference.
I’m excited to join SOCi and Google for an exclusive webinar, Winning the Next Era of Local Visibility, on June 3. It’s a golden opportunity for anyone looking to stay ahead of the curve.
During this webinar, I look forward to learning:
How AI is transforming local search dynamics.
The types of signals that AI considers for recommendations.
Strategies to boost visibility on Search, Maps, and Gemini.
The implications of Ask Maps for your brand.
I’m convinced that AI is already shaping customer discovery, so it’s crucial to ensure your business isn’t left behind.
I’ve noticed that more and more of us are finding ourselves suddenly and, at times, permanently locked out of our Facebook accounts. What used to be just an occasional issue has turned into a widespread frustration impacting not only everyday users but creators and business owners as well.
So, what’s driving this increase? It’s a mix of AI moderation, enhanced security protocols, platform dynamics, and changing user habits. Let’s dive into the underlying factors behind this trend.
The rise of AI moderation — and its tradeoffs
At the core of this issue is Meta, Facebook’s parent company, which relies heavily on artificial intelligence to oversee user activities across billions of accounts. These AI systems are tasked with:
Identifying harmful content,
Thwarting scams and abuse,
Enforcing community standards at scale.
However, there’s a significant tradeoff with AI moderation. Unlike humans, AI struggles to grasp context and nuance, which often leads to:
Flagging normal behavior as suspicious,
Misinterpreting the context of communications,
Imposing account restrictions based on patterns instead of intentions.
This has triggered an increase in false positives, where users find themselves unjustly locked out. Reports of wrongful account deactivation are rampant, typically due to AI-only moderation with little human oversight. Astonishingly, appeals can sometimes be resolved immediately, hinting at minimal human involvement despite official policies.
Account takeovers are increasing
With the surge in cybercrime over recent years, social media platforms have increased their security measures. Facebook now deploys more aggressive signals to spot:
Logins from unfamiliar locations or new devices,
Frequent changes to account settings,
Unusual messaging or posting patterns.
While these steps aim to block malicious actors, they also come with unintended side effects:
Travel, using a VPN, or device changes can cause lockouts,
Legitimate users may be snared alongside malefactors.
When hackers access an account, they often alter the registered email and password, activating security alerts and locking the original owner out entirely. From Facebook’s viewpoint, the account is indeed compromised; however, recovery processes don’t always fast-track access back to the rightful owner.
The role of new features and identity verification
In recent years, Facebook has introduced new security layers, including:
Two-factor authentication,
Identity verification checks,
Paid support options connected to account verification.
While these features enhance security, they also introduce complications, making account recovery more cumbersome:
Adding steps to recover accounts,
Creating barriers for users who struggle with identity verification,
Causing lockouts when verification fails.
Some users report being asked to submit identification several times without resolution, escalating the frustration.
The business incentive behind platform changes
Meta’s motivations for investing in AI moderation and automated enforcement boil down to cost-effectiveness. Automation provides instant scalability, reduces operational expenses, and manages ‘standard’ cases effectively. However, this efficiency comes at a price. For those outside agencies or larger entities operating within Business Manager, finding significant support can be a challenge — leaving some of us without a clear path for escalation.
Meta’s commanding position in the social media advertising space, coupled with robust financial performance and political influence, leads to minimal external pressure to reform its support systems. Meanwhile, search queries related to account recovery are often dominated by Meta’s resources, directing users back into the same narrow support ecosystem, even when alternative solutions might exist.
Platform scale is working against users
One can’t ignore the sheer enormity of Facebook’s operations. With a global user base of billions, even minor error rates can affect millions of individuals. Consequently, Meta’s support systems can’t possibly offer personalized support to everyone, leading to automation as the norm, despite its imperfections.
Additionally, internal fragmentation complicates matters further. Facebook isn’t a singular system — it’s an expansive ecosystem including personal profiles, Pages, ad accounts, Business Manager, and platforms like Instagram, Threads, and WhatsApp. Each operates with distinct rules and support channels. When issues traverse multiple systems — as they often do — no single team fully ‘owns’ the problem, making resolutions slower, more complex, and harder to navigate.
What can seem like a deeply personal problem is often the result of a system optimized for global efficiency, sometimes at the expense of individual support. Facebook aims to minimize risk on a large scale, which can clash directly with the need for prompt, personalized support.
Lack of human support and regaining access
One of the ongoing frustrations isn’t just the lockouts but what follows them. Many users, including myself, face challenges such as:
Limited access to human support,
Automated replies that fail to address the issue,
Confusing or ineffective recovery workflows.
Although Meta is introducing new support tools, much of the assistance process remains automated. If your problem doesn’t fit perfectly into one of their defined categories, resolution becomes even more challenging.
This is primarily because Facebook’s support system is structured around rigid, predefined pathways like “my account was hacked,” “I can’t log in,” or “my ad was rejected.” But most issues don’t neatly fit into one of these categories. They’re often multifaceted: part hack, part lockout, or linked to both personal and Business Manager accounts, further complicated by unclear or incorrect policy flags.
When my situation doesn’t match a single category, the system struggles to process it correctly. Instead of progressing towards a solution, I’m often routed through repetitive workflows — submitting forms that don’t entirely apply — leaving me trapped in exhausting loops without a clear way forward.
William Jennings, who runs WKJ Consulting, a social account recovery consultancy, has observed how these gaps have led to an underground recovery market. Some dubious services even exploit locked-out users by demanding payments through unconventional means like game credits — a problem that persists because legitimate recovery channels remain limited.
Accounts that link through Meta’s Account Center (including Facebook and Instagram) generally have a more straightforward recovery process. Sometimes, users can subscribe to Meta Verified on a linked Instagram account to access chat support and initiate an administrative claim.
Jennings highlights that:
“Meta Verified acts almost like paid protection — approximately 90% effective in preventing wrongful restrictions or disabling, though it doesn’t offer a guarantee if the rules are violated.”
A well-structured recovery method often involves:
Subscribing to Meta Verified to gain chat support,
Filing an administrative dispute with necessary documentation (such as error screenshots, emails, account URL, and ID verification),
Escalating to legal support in more acute scenarios.
It’s crucial that hacked accounts follow dedicated channels like facebook.com/hacked or instagram.com/hacked, and it’s far more effective to focus on prevention than recovery.
After regaining access, it’s essential to undertake steps like enabling two-factor authentication, saving recovery codes, and adopting advanced security measures.
Enforcement has scaled — recovery hasn’t
Facebook lockouts are an inherent consequence of the platform’s development. As Meta continues to emphasize automation and efficiency, many of us engage with systems built for speed, security, and risk minimization.
Most of the time, these systems function silently in the background. But when they falter, it feels abrupt, opaque, and incredibly hard to navigate.
Access to meaningful support often correlates with high ad spend, established business accounts, and tied to paid verification products. This leads to an unbalanced support landscape where major advertisers receive better assistance, leaving individuals and small businesses with fewer options.
For a platform operating on a global scale, this setup is intentional. But for those entangled in the process, it’s incredibly frustrating.
