When I discovered Google’s latest update to the Merchant Center, I was thrilled. They’ve added a ‘build to order’ option for vehicle listings, offering sellers like me a streamlined way to display customizable models that customers can factory-order.
I immediately saw how this attribute could revolutionize my listings. It’s designed for dealers who, like myself, don’t always have every model available on the lot. This addition allows us to tag vehicles that aren’t in stock but can be tailored and ordered. It’s a game-changer!
What needs to change. I’m aware that updating my listings involves two critical steps. First, I need to adjust my structured data by setting availability to BuildToOrder. Secondly, I must align my Merchant Center feed with the same availability code. Ensuring consistency is key to avoid listing disapprovals.
Instruction on when to use the availability [availability] attribute in GMC
Why we care. This update is a breath of fresh air for us sellers. Until now, conveying a vehicle’s unavailability for immediate pickup was challenging. Now, the ‘build to order’ option clearly mirrors the operations of modern automakers, especially those like Tesla and Rivian that offer direct-to-consumer customization. It helps set clear expectations for our customers and ensures our data is pristine for Google.
The fine print. Remember, if a vehicle is categorized as ‘build to order,’ it must have the condition attribute set to ‘new.’ If it’s listed as ‘used,’ it will be disapproved. Google regards build-to-order vehicles as newly configured, not pre-owned.
Bottom line. For anyone like me selling customizable or factory-order vehicles, this update is a more precise way to reflect vehicle availability. However, it only works if my feed, structured data, and condition fields are in synchronization.
I first learned about this update from Google Shopping specialist Emmanuel Flossie, who kindly explained how to implement it on his blog.
If you lead AI search, SEO, content, or marketing technology, Profound’s funding can create immediate pressure. Is the company now the category winner? Is your team late? Should you add another platform to your stack? The financing matters, but none of those conclusions follows automatically.
Read the round correctly before changing your strategy
A funding round is evidence that investors were willing to finance a company on negotiated terms. It is not a product certification, an independent performance test, or proof that customers are receiving a positive return.
The distinction matters because the headline contains several figures that are easy to misread. The $96 million is financing, not revenue. The $1 billion valuation is the value assigned to the company in the context of the transaction, not cash deposited into its accounts. Neither figure tells you how much customers spend, whether the business is profitable, how well its software performs, or how the new capital will be allocated.
Series C also describes a financing stage, not a universal level of product maturity. It can support expansion after earlier growth, but the label does not guarantee stable data, complete model coverage, enterprise-ready controls, or a roadmap that matches your needs.
What the round establishes: Profound has attracted substantial private backing for an AI marketing platform.
What it reasonably signals: the participating investors see enough potential to finance further growth at the announced valuation.
What it does not establish: that Profound is the right platform for your use case, that AI visibility software has settled on a standard methodology, or that a large valuation predicts your results.
That last point should shape your response. Do not rewrite your AI strategy around a financing headline. Use the event as a reason to update your assumptions, inspect the category, and ask vendors harder questions.
Where fresh capital could change the AI marketing market
Capital gives Profound more options. It could fund product development, infrastructure, model and market coverage, integrations, hiring, customer support, or go-to-market expansion. Those are possibilities, not disclosed commitments. Treat them as items to verify through shipped capabilities, release records, service levels, and written commercial terms.
The broader signal is that investors are willing to place significant capital behind the problem of marketing through AI-generated answers. That is relevant if you have been treating AI visibility as a temporary reporting experiment. It suggests that the category may attract more product development, sales activity, and competition. One transaction, however, does not establish the size of customer demand or prove that AI search has replaced conventional search.
Your operating model should therefore connect AI visibility to the rest of search and content work instead of building an isolated dashboard. A useful workflow has four linked jobs:
Observe: identify where your brand, products, experts, and pages appear or disappear in relevant AI answers.
Diagnose: determine whether the issue involves ambiguous entities, missing evidence, inaccessible content, inconsistent facts, weak third-party corroboration, or an irrelevant prompt sample.
Intervene: improve the assets you control, including factual copy, source pages, technical accessibility, appropriate structured data, and evidence that other publishers can verify.
Validate: repeat the measurement, inspect the underlying answers and citations, and connect any change to a business decision rather than celebrating a score in isolation.
A platform that performs only the observation step may still be useful, but it has not completed the marketing job. The value appears when your team can trace a detected issue to a defensible action and then check whether that action changed anything meaningful.
Use a buyer’s scorecard, not the valuation
If you are evaluating Profound or another AI visibility platform, apply the same scorecard to every vendor. This prevents brand momentum, investor names, and polished aggregate scores from substituting for evidence.
Start with measurement integrity. Ask which AI models and user experiences are covered, which markets and languages are supported, and whether the results represent live answers, an external data provider, or another collection method. Model output can vary with prompt wording, model version, user context, and repeated runs. You need to know how the platform handles that variability before treating movement as a trend.
How are prompts selected, grouped, weighted, and updated?
Can you inspect the exact prompt, answer, cited pages, collection time, and relevant execution context behind every score?
Does the system distinguish a brand mention from a recommendation, a citation, a comparison, or a factual statement?
How does it prevent changes in prompt coverage from looking like changes in brand performance?
Can you preserve a stable benchmark while separately exploring new prompts and models?
How are failed collections, unavailable models, duplicate answers, and ambiguous brand names handled?
A visibility score that cannot be decomposed is difficult to act on. If the score rises, you should be able to see which answers changed and why. If it falls, you should be able to distinguish a real deterioration from a collection or coverage change.
Then test actionability. Ask the vendor to walk from a detected problem to a recommended intervention using your own data. A useful recommendation identifies the affected audience, the evidence behind the diagnosis, the asset or relationship that needs work, the owner who can act, and the signal that would count as improvement.
Does the platform separate issues on your website from gaps in third-party authority?
Can recommendations point to the exact pages, claims, citations, or entity conflicts involved?
Does it explain where structured data is relevant without presenting schema as a guarantee of inclusion in an AI answer?
Can findings flow into the content, SEO, analytics, public relations, and product workflows your team already uses?
Can analysts annotate changes so later reporting does not confuse an intentional intervention with unexplained movement?
Finish with commercial and operational resilience. Funding may improve a vendor’s capacity to invest, but it does not remove switching costs or contractual risk. Get data ownership, export access, retention, usage limits, overage rules, support scope, renewal terms, and the total expected cost in writing. Confirm what happens to your historical data if you leave. Treat roadmap slides as possibilities until a capability is included in the agreement or available in the product.
Run a controlled evaluation around a real decision
The cleanest way to evaluate an AI marketing platform is to make it answer a decision your team already faces. Do not begin with, “Can this produce an interesting dashboard?” Begin with a question such as, “Can this show us why qualified buyers encounter competitors instead of us, and can it help us choose what to change?”
Define the decision. Name the audience, product or service, market, and business question. Decide who will act if the platform finds a credible problem.
Create a representative prompt set. Include branded and unbranded questions from different stages of the buying journey. Write down why each prompt matters. Keep the core set stable so a changing sample does not masquerade as performance movement.
Capture a manual baseline. Save the exact prompts, visible answers, citations, model or surface, and relevant context. Note entity ambiguity and obvious collection errors before introducing a vendor score.
Run the platform against the same scope. Compare its output with the baseline. Investigate disagreements rather than assuming the platform or the manual sample is automatically correct.
Act on findings you can verify. Correct inconsistent facts, strengthen useful first-party pages, improve crawlability, add appropriate structured data, and pursue credible third-party coverage where the diagnosis supports those actions.
Judge decision value. Ask whether the platform found important issues accurately, explained them clearly, helped the right owner act, preserved evidence, and made follow-up measurement more reliable.
Keep AI visibility metrics in their proper place. Mentions, citations, answer share, and sentiment can be useful intermediate signals, but they are not automatically revenue or causation. If a dashboard improves after you change content, inspect the underlying answers. If business outcomes also change, examine other campaigns, seasonality, brand activity, and measurement gaps before assigning credit.
Be equally cautious with promises of fixed placement. Generative answers are not conventional ranking tables, and their behavior can change. A credible evaluation should show variability, preserve raw evidence, and describe uncertainty instead of hiding it inside a single precise-looking number.
Key takeaways
Profound announced a $96 million Series C and a $1 billion valuation, with Lightspeed Venture Partners leading the round.
The financing signals investor conviction and gives the company more strategic options; it does not prove product performance, revenue, profitability, or customer return.
For AI marketers, the round is a reason to take the category seriously, not a reason to replace a working stack without evaluation.
A useful AI visibility platform must expose prompts, answers, citations, collection context, and methodology behind its scores.
Your evaluation should connect observation to diagnosis, intervention, and validation using a stable prompt set and a manually checked baseline.
Commercial diligence still matters: verify exports, data ownership, limits, support, renewal terms, switching costs, and delivered capabilities before making a long-term commitment.
Treat Profound’s funding as a prompt to sharpen your vendor questions, not to change strategy overnight. Preserve your baseline, test the platform against a decision that matters, and commit only when its data survives manual inspection and fits the way your team acts. That lets you benefit from a better-funded category without outsourcing your judgment to its valuation.
I find Reddit’s new pilot program fascinating. They’re using AI to transform our beloved community recommendations into interactive, shoppable product carousels within search results.
What’s happening: Right now, a select group of U.S.-based folks, including myself, might notice these exciting product carousels popping up in search results whenever our queries suggest a buying intent, like when searching for “best noise-canceling headphones” or “top budget laptops.”
These carousels conveniently appear right at the bottom of the search results, showcasing pricing, images, and direct links to retailers. The coolest part? These products are derived from actual Reddit posts and comments rather than existing ad inventories.
For those of us interested in consumer electronics, Reddit also collects data from specific Dynamic Product Ads (DPA) partner catalogs.
How it works: The AI cleverly identifies queries with purchase intent, scans through relevant Reddit discussions for any product mentions, and arranges them into tidy, shoppable cards. When a card catches my attention, I can simply tap it to gain more information or be redirected to a retailer.
Why we care: These shopping carousels are a real game-changer for advertisers. They bring products to the spotlight right when consumers, like me, are contemplating a purchase and seeking peer approval. Unlike typical ads, here these products merge with Reddit’s trusted community vibe, making them seem more like genuine recommendations than mere advertisements.
For brands already involved in Dynamic Product Ads on Reddit, this development offers a seamless pipeline from community buzz directly to action.
Between the lines: Reddit is really onto something big here, doing what many competitors have struggled to achieve—using organic, community-driven content as the foundation for a shopping experience, rather than depending solely on targeted advertising.
This approach is ingenious because consumers, myself included, are becoming warier of sponsored content. Reddit’s value relies on authentic community engagement, and by integrating that into a shopping feature, it elevates their credibility beyond traditional retail media networks.
The big picture: Retail media is booming, and platforms catering to audiences with high purchase intent are in a race to claim their portion of the pie. With Reddit’s increasing search traffic, especially after partnering with Google, this development seems like the perfect next step.
The bottom line: Reddit is testing how it can turn search intent directly into transactions, making it smoother for users like me to transition from recommendations to purchase, all while staying within the community context that fosters trust.
I’ve recently discovered an exciting development in Google Ads that’s set to revolutionize how we track and measure our advertising success. The platform is now testing a beta feature that allows us to link external data sources directly into the conversion action settings. This move aims to strengthen the bridge between our first-party data and campaign measurement.
How does this work, you might ask? In the conversion action details, a new section titled “Get deeper insights about your customers’ behavior to improve measurement” encourages us to connect our external databases to our Google tag, offering a seamless integration experience.
This integration supports platforms like BigQuery and MySQL, with the primary goal of enriching our conversion metrics and enhancing performance signals. Notably, this feature is highlighted within the data attribution settings and is gradually being rolled out in its Beta phase.
Why do we care? The ability to directly integrate these data sources reduces the hassle of syncing offline or backend data with ad measurements. This beta feature from Google Ads simplifies connecting first-party data to conversion tracking, improving our measurement accuracy and campaign optimization.
By harnessing the power of platforms like BigQuery or MySQL, we’re able to incorporate richer customer data into our signals, crucially offsetting any data loss resulting from recent privacy changes. In practical terms, this means smarter bidding, clearer attribution, and the potential for a stronger ROI.
Beneath the surface, embedding these data connections directly within conversion settings—rather than relying on separate pipelines—democratizes advanced measurement tactics, making them accessible not only to large enterprises but to advertisers like you and me.
As ad platforms compete for superior measurement accuracy, these native data integrations are emerging as a pivotal advantage, particularly for brands heavily investing in proprietary customer data.
I’ve noticed something quite unexpected happening with Google Ads lately. It seems that their system tool is re-enabling paused keywords automatically, which has led to increased campaign expenses without warning.
Some advertisers, including myself, have observed a Google Ads tool—created for low-activity bulk changes—unexpectedly switching paused keywords back to active. This unusual behavior has been a surprise to many account managers, like myself, who haven’t come across this issue before.
What’s happening? The activity logs are showing entries linked to Google’s ‘Low activity system bulk changes’ tool executing actions that enable previously paused keywords. These logs appear as automated bulk updates and, thankfully, have an ‘Undo’ option available.
In the past, this tool mainly paused inactive elements rather than reactivating them, so this change in behavior is quite perplexing.
What’s unclear? Google hasn’t issued any public documentation to explain this behavior, leaving us unsure whether it’s an intentional feature, a limited test, or a mere bug.
I find myself wondering what exactly triggers this reactivation and how widespread this phenomenon is becoming.
Why does this matter? If like me, you’re diligently managing your campaigns, unexpected keyword reactivation can change your campaign delivery in ways you didn’t plan for, impacting budgets, pacing, and overall performance—particularly if you’ve paused keywords for a specific reason.
For both agencies and in-house teams, this change is raising concerns about automated systems potentially overriding manual settings.
What steps should we take now? As account managers, we might want to regularly check change histories, be on the lookout for any unexpected keyword activations, and use the ‘undo’ function promptly if we notice unplanned changes.
Until Google clarifies the situation, more careful monitoring of campaigns relying heavily on paused keywords might be necessary.
First Alerted This issue was first brought to light by Performance Marketing Consultant Francesco Cifardi on LinkedIn.
I recently discovered how crucial first-party data has become in the evolving landscape of AI-powered advertising. It’s fascinating to see how it shapes the optimization and measurement of automated ad campaigns.
During a chat with Search Engine Land, I learned from Julie Warneke, CEO of Found Search Marketing, about the profound impact first-party data has on profitable advertising, regardless of potential changes to Google’s third-party cookie policies.
Embracing first-party data means tapping into customer information that I own, typically stored in a CRM, like lead details, purchase history, revenue, and customer value collected from various touchpoints.
This type of data is distinct from platform-owned or browser-based data, over which I have limited control.
Digital advertising has evolved over the years. The shift from focusing on impressions and clicks to outcomes emphasizes profitable conversions, according to Warneke. Advertisers who provide AI systems with quality customer data gain a significant edge.
Although rising cost-per-clicks (CPCs) are inevitable in paid media, first-party data enhances conversion quality, revenue, and return on ad spend, making higher costs justifiable with better results.
By leveraging first-party data tied to revenue and customer value, AI bidding systems can target users resembling high-value customers, even beyond usual demographic or geographic signals, leading to better conversions.
Among campaign types, Performance Max (PMax) thrives with first-party data activation. It performs best when I shift from manual optimizations to feeding it accurate data, allowing the system to learn, as Warneke highlighted.
Even small and mid-sized businesses can leverage first-party data, as seen in Warneke’s examples of success with small customer lists. The challenge lies in setting up proper infrastructure for tracking, consent management, and data flow.
Common mistakes include weak data capture, where brands rely on browser-side tracking that falters on platforms like iOS, and broken feedback loops from sporadic CRM data uploads. Continuous data streams are crucial.
Warneke advises taking a step back to audit how data is captured, stored, and relayed to platforms. Incremental improvements can pave the way for significant long-term gains, even starting with a small portion of a budget as a test.
Ultimately, AI optimization reflects the quality of signals received. By refining first-party data, I can influence outcomes favorably, avoiding inefficiency risks.
Managing my website’s URLs efficiently is crucial to prevent crawlers from slowing it down. If you’re like me, you want your site to load fast, ensuring both visitors and search engines have a seamless experience.
Just the other day, I listened to Google’s latest insights on their year-end report for 2025. It was fascinating to hear Gary Illyes discuss on the Search Off the Record podcast about the major crawling challenges Google faces, like faceted navigation and action parameters, which make up a whopping 75% of the issues.
What’s the issue? Well, I’ve learned that crawling problems can seriously impact site performance, potentially making it unusable or inaccessible. Crawlers can sometimes get stuck in an infinite loop on a site, wreaking havoc on server performance.
According to Gary, once a set of URLs is discovered, the crawler has to check a significant portion to determine its quality. By the time this is done, the damage is done—your site slows down dramatically.
The Biggest Crawling Challenges Here’s what caught my attention as the major issues from the report:
50% relate to faceted navigation. These are very common in e-commerce sites where endless filtering options exist for products based on size, color, price, etc.
25% pertain to action parameters. These come from URL parameters that trigger actions instead of significantly changing page content.
10% involve irrelevant parameters like session IDs or UTMs.
5% are due to plugins or widgets that cause confusion by creating problematic URLs.
2% encapsulate other “weird stuff”, which includes strange issues like double-encoded URLs.
Why this matters to me is simple. A well-structured URL strategy keeps my server healthy, ensures quick page loads, and prevents search engines from misunderstanding which URLs should be indexed as canonical.
The Podcast: Here’s where you can listen to the discussion yourself:
Analyzing nearly two million LLM sessions across nine industries throughout 2025 was a fascinating journey for me. I began with the assumption that ChatGPT would dominate and that AI usage patterns would be relatively uniform with minimal impact.
The findings, however, were surprising.
While ChatGPT does indeed control 84.1% of the trackable AI discovery traffic, it’s primarily serving as a broad-market tool. This discovery significantly impacts strategic approaches.
In today’s landscape, relying solely on a single discovery strategy is not viable. A multi-platform approach that aligns with how and where users find productivity is essential.
Brands must now discern which platforms are empowering productivity rather than merely supporting initial discovery phases.
Various LLMs are excelling in different sectors, often with stark differences. The key takeaway for 2026 is more complex than simply focusing on ChatGPT.
Here’s what I’ve discovered from the data.
The Growth Rate Divergence: ChatGPT vs. Competitors
Throughout 2025, major LLM platforms exhibited significant growth discrepancies:
ChatGPT: 3x growth
Copilot: 25x growth
Claude: 13x growth
Perplexity: 1x growth
Gemini: 1x growth
Although ChatGPT grew, Copilot and Claude experienced much more rapid growth. Platforms like Perplexity and Gemini remained steady, reinforcing specific workflows.
These numbers highlight strategic priorities:
Satya Nadella celebrated Copilot reaching 100 million monthly users.
Dario Amodei revealed that Anthropic’s revenue grew from $100 million to $8–10 billion in under two years.
Aravind Srinivas noted significant interest in Perplexity Finance.
The focus on growth is crucial because it signals true user value:
Copilot excels in the Microsoft ecosystem.
Claude appeals to developers.
Perplexity thrives among finance professionals.
Different LLMs are thriving in various industries at markedly different rates.
Pattern 1: Copilot’s Striking Growth
Copilot’s remarkable 25x growth is indicative of its premier position in B2B environments reliant on Microsoft tools.
SaaS
ChatGPT: 2x growth
Copilot: 21x growth
The rapid adoption mirrors modern SaaS practices, embedding LLMs directly into workflows.
Education
ChatGPT: 6x growth
Copilot: 27x growth
Copilot benefits from educational settings fostering knowledge sharing and synthesis.
Finance
ChatGPT: 4.2x growth
Copilot: 23x growth
Finance aligns with Copilot due to automation needs and context dependency.
Copilot’s growth is most pronounced in industries where professionals are deeply integrated with Microsoft tools.
Instruments like Excel transform into data interpretation powerhouses with Copilot, eliminating the need for external searches.
Implications
For work-centric audiences like SaaS, finance, and education specialists, AI discovery is shifting into LLMs embedded in workflows.
Pattern 2: Perplexity Shines in Finance
While Perplexity has flat growth overall, it stands strong in finance with a 24% market share, unlike in other sectors where it has diminished.
SaaS: down to 7.3%
E-commerce: down to 3.4%
Education: down to 5.2%
Publishers: down to 3.6%
Finance demands accuracy; thus, traceable sources make Perplexity vital in this sector.
Partnering with Benzinga, FactSet, and others, Perplexity offers in-depth data vital for financial decisions.
Trust and verifiability are crucial in finance, and that’s where Perplexity excels.
Implications
In finance, selection of platforms that integrate with licensed data and credible sources is critical. Success hinges on being part of these authoritative ecosystems.
Pattern 3: Claude’s Dominance in Analysis
With just a 0.6% share, Claude might appear to be an underdog, but it thrives in specialist sectors like publishing and finance.
Publishers: 49x growth
Education: 25x growth
Finance: 38x growth
SaaS: 10.3x growth
Claude’s strength lies in standalone, strategic thinking rather than integrated tools like Copilot.
Publishing professionals and financial analysts use Claude for its substantial context window, enabling complex and strategic queries.
Implications
Target audiences that require in-depth analysis should focus on creating structured and detailed content. Claude’s user base is smaller but highly influential.
Pattern 4: Challenges in Tracking Gemini
The data concerning Gemini is puzzling, showing both growth and declines. This could be attributed to issues with attribution rather than an actual decline in users.
Education: −67% tracked traffic
SaaS: +1.4x growth
Finance: +1.3x growth
E-commerce: +2.7x growth
Gemini’s interaction model keeps users within its ecosystem, making measurement challenging.
The reality is that usage might still be robust, but the tracking systems need to catch up with user behaviors.
Implications
As AI-assisted conversions increasingly occur, traditional last-click attribution models need reconsideration.
Monitor brand search performance and invest in broader visibility strategies.
Strategizing Your LLM Approach
AI discovery is diversifying rather than converging. Tailoring strategies based on your audience’s preferences and behaviors is crucial.
Enterprise Audiences: Focus on Copilot integration for SaaS and B2B environments.
High-Stakes Decisions: Consider Perplexity’s reliability in providing traceable data.
I’ve noticed the European Union is turning its gaze towards Google once more, scrutinizing how it handles its AI and search data. This could lead to changes that might open up its Android features and search data, ultimately reshaping the competitive landscape.
The European Commission is now formally outlining the ways Google must share specific Android functionalities and its search data with competitors, in line with the Digital Markets Act.
Tuesday marked the start of two official proceedings by the Commission, aimed at establishing a structured approach for Google to meet key obligations under the DMA. It’s fascinating to see these regulatory dialogues become more concrete.
Why I care. This move by the European Commission could alter the dynamics in mobile AI and search. With Google potentially needing to share its search data and Android AI capabilities, it could boost the competition from other search engines and AI services. Such changes might impact where advertisers allocate budgets, alter the availability of advertising inventory, and shift campaign dependencies away from Google’s platforms.
First focus — Android and AI interoperability. The regulators are delving into how Google must enable third-party developers to access Android hardware and software features as freely as Google’s own AI services, like Gemini.
– The objective is to allow rival AI providers the same level of integration with Android devices as Google’s native tools.
Second focus — search data sharing. The Commission aims to define how Google should provide anonymized search data including ranking, queries, clicks, and views to rival search engines under fair, reasonable, and non-discriminatory conditions.
– This includes specifying the types of data to be shared, how it will be anonymized, eligibility for access, and whether AI chatbot providers can use this dataset.
Between the lines. It’s not just about ticking off compliance boxes. The Commission is making it clear that AI services are under the DMA’s watchful eye, especially where data and device control could influence emerging markets.
What’s next: Within three months, the Commission plans to send Google its initial findings and recommended actions. The full proceedings should wrap up within six months, accompanied by non-confidential summaries for public input.
The backdrop. Since March 2024, Google has been required to comply with DMA obligations, having been identified as a gatekeeper in services like Search, Android, and YouTube.
Bottom line. The EU is moving from planning to action with the DMA, testing how strongly it will influence competition by overseeing Google’s AI functions and search data management.
I’ve decided to transform my expertise in SEO into a powerful fundraising initiative to assist those affected by recent ICE raids in Minnesota. Instead of standing by, I’m trading my consulting hours for donations to support immigrant families in need.
The tipping point for me came when recent events in Minnesota crossed ethical lines I had drawn. I felt a strong urge to act rather than just watch from the sidelines. I shared my initiative on LinkedIn and my blog, inviting the community to join this cause.
What’s happening. I’m leveraging my skills by offering my services in return for donations through GiveMN. This Minnesota-based platform channels funds to families and individuals hit hardest by the ICE raids.
Within just seven hours, we raised $1,850, which soon increased to $1,950. It’s heartwarming to see backing from renowned SEO agencies, SaaS companies, and individual practitioners rallying behind this cause.
Why we care. My efforts showcase a vital aspect of the search marketing industry: our community’s ability to rally resources for broader social causes. This isn’t just about professional skills; it’s about standing up for humanity and activating swift collective action.
Catch up quick. The fundraiser springs from widespread outrage following the launch of Operation “Metro Surge” by federal immigration authorities in December. This operation deployed roughly 3,000 ICE and Border Patrol agents into the Twin Cities, resulting in significant unrest.
The operation triggered issues like racial profiling, unwarranted home invasions, detentions at workplaces, and tragically, the shooting of 37-year-old Renee Nicole Good in downtown Minneapolis, sparking massive protests.
What I’m saying. As I put it, “This is NOT about politics. This is about treating all people as humans.” It’s a call to action to see beyond political lines and focus on our shared humanity.
