Tag: AI

  • Anthropic Profitability and IPO Outlook: What to Watch

    Anthropic Profitability and IPO Outlook: What to Watch

    If you are weighing Anthropic ahead of a possible IPO, the central question is not whether its revenue is growing. It is whether the company can turn that growth into durable profit after compute, cloud-partner fees, model training, stock compensation, and every other consequential cost are counted.

    The available numbers point to a sharp improvement, but they remain third-party estimates rather than audited public-company results. Anthropic appears to have crossed an important profitability threshold. That makes the business more IPO-ready; it does not tell you whether the eventual shares will be attractively priced.

    Key takeaways

    • Anthropic is estimated to have reached adjusted operating profit in Q2 2026, producing $570 million on $11.6 billion of quarterly revenue, before increasing that profit to $940 million in Q3.
    • Its estimated gross margin rose from 21% in Q1 2025 to 57% in Q3 2026, while compute cost fell from $2.41 to $0.54 per dollar of revenue. That combination, rather than revenue growth alone, explains the profit turn.
    • The frequently cited $69.7 billion revenue figure is an August 2026 annualized run rate, not revenue already earned over a full year. The 2026 full-year revenue forecast is $56 billion.
    • Adjusted profit excludes stock-based compensation and other charges that can materially affect GAAP results. An IPO filing will need to show the reconciliation, cash flow, compute commitments, customer concentration, and fully diluted share count.
    • Even a strong operating business can be a poor investment at the wrong valuation. The offering price matters just as much as the growth story.

    The profit turn is meaningful, but the definition matters

    Anthropic’s estimated quarterly progression shows more than a company growing its way out of a fixed-cost base. It shows improving unit economics. Gross margin measures revenue after the cost of serving models, while compute cost per dollar of revenue also incorporates the cost of training new models. Adjusted operating income then subtracts operating expenses but excludes stock-based compensation.

    The change across five representative quarters is substantial:

    QuarterEstimated revenueGross marginCompute cost per $1 of revenueAdjusted operating incomeAdjusted operating margin
    Q1 2025$0.41B21%$2.41-$1.58B-385%
    Q4 2025$2.01B38%$1.27-$2.47B-123%
    Q1 2026$4.20B43%$0.73-$1.93B-46%
    Q2 2026$11.60B52%$0.58$0.57B4.9%
    Q3 2026$17.30B57%$0.54$0.94B5.4%

    These are modeled figures covering January 2025 through September 2026. They should be treated as a directional view until official financial statements confirm them.

    Three things are happening at once. Quarterly revenue expanded from $4.2 billion to $11.6 billion between Q1 and Q2 2026. Gross margin crossed 50%. Compute cost per revenue dollar continued falling even as the business grew. If revenue had increased while compute efficiency remained stuck at its early-2025 level, the company would still have been spending more on compute than it generated in revenue.

    The caution is in the final column. A 5.4% adjusted operating margin leaves only a little more than five cents of adjusted operating profit per revenue dollar. That is a real milestone, but not a large buffer against price reductions, higher usage, partner costs, or another increase in training expenditure.

    The annual swing is even more dramatic. Anthropic is estimated to have lost $7.98 billion on $4.62 billion of revenue in 2025. The 2026 projection calls for $1.19 billion of adjusted operating income on $56 billion of revenue, a margin of 2.1%. Because that full-year outcome includes a forecast for Q4 and excludes stock compensation, it should not be mistaken for confirmed GAAP profitability.

    When an IPO filing arrives, go directly to the reconciliation between adjusted and GAAP operating income. Record the stock-based compensation, financing-related charges, and any expense classifications excluded from management’s preferred measure. If the profitable result disappears after those items, describe Anthropic as adjusted-profitable rather than simply profitable.

    Run-rate revenue is the number most likely to be misread

    A stream of coins passes through a measuring chamber while a glowing projected path extends beyond the smaller amount physically accumulated.

    Run rate takes one month’s revenue and multiplies it by 12. It answers a useful but narrow question: what would annual revenue look like if that month’s pace continued unchanged? It does not mean the company collected that amount during the preceding year, and it does not guarantee that the pace will continue.

    Anthropic’s estimated annualized run rate increased from $5.8 billion in September 2025 to $69.7 billion in August 2026. The largest monthly jump came between April and May 2026, when the run rate rose by $18.5 billion as several large enterprise agreements began billing.

    That billing pattern is precisely why you should keep three different figures separate:

    1. $17.3 billion is estimated revenue booked during Q3 2026.
    2. $56 billion is the forecast for revenue across the full 2026 calendar year.
    3. $69.7 billion is August 2026 revenue annualized as though one month’s pace persisted for 12 months.

    Run rate is not useless. In a business growing this quickly, trailing revenue can materially lag the latest sales pace. The mistake is applying a valuation multiple to annualized monthly revenue without testing whether new contracts recur, whether usage is committed, and whether a small number of customers caused the jump.

    For your eventual IPO analysis, use reported trailing revenue as the main valuation denominator. Keep run rate as a momentum indicator. Then compare both with remaining contractual obligations, customer concentration, renewal data, and revenue recognized from minimum commitments rather than actual usage. That prevents a strong month from silently becoming a full-year assumption.

    Revenue mix will decide whether margins keep improving

    Anthropic does not earn the same margin on every dollar. Its Q2 2026 estimates show a 35-percentage-point spread between the highest- and lowest-margin business lines:

    Business lineShare of Q2 2026 revenueEstimated gross marginWhat to watch
    Direct API33.8%64%Whether price per token falls faster than inference cost
    Cloud partner API23.1%34%Partner fees, accounting presentation, and channel mix
    Claude Code19.4%48%Compute consumed by long agentic sessions
    Team and Enterprise seats12.6%69%Usage per seat, renewals, and contract durability
    Pro and Max subscriptions11.1%39%Heavy-user economics and subscription pricing

    The Q2 mix produced a blended gross margin of 52%. Team and Enterprise seats led at 69% because a fixed per-seat price exceeded average usage cost. Direct API revenue followed at 64%. Cloud partner API revenue, sold through Amazon Bedrock, Google Cloud Vertex AI, and Microsoft Foundry, carried the lowest margin at 34%.

    The cloud-partner number contains an accounting issue that matters for valuation. Anthropic is understood to record partner sales at the full price paid by the customer and record the partner’s share as a cost. A company recognizing the same transaction net would report lower revenue and a higher gross-margin percentage even if the underlying cash economics were identical.

    That does not make either presentation inherently wrong. It does mean a revenue multiple can create a misleading comparison between companies with different channel accounting. Compare enterprise value with both revenue and gross profit, and check the eventual accounting policy before treating Anthropic’s top line as directly comparable with a competitor’s.

    Mix can move margins in either direction. More Team and Enterprise seat revenue should help while average usage remains below the pricing ceiling. More cloud-partner revenue can expand distribution but dilute reported gross margin. Claude Code sits between those outcomes: it represented 19.4% of Q2 revenue at a 48% gross margin, with longer agentic sessions consuming more compute than ordinary API requests.

    Claude Code’s share stayed between 15% and 21% of company run-rate revenue from September 2025 through August 2026. It grew with Anthropic rather than separating from the rest of the business. Watch its gross margin and retention, not just its revenue, because rapid adoption is less valuable if increasingly long sessions absorb the incremental dollars.

    What the IPO filing needs to prove

    A transparent AI business engine with computing, customer, cash, and cost components is examined under lenses before a closed public-market doorway.

    The optimistic financial path assumes that inference hardware becomes cheaper per token and training expenditure grows more slowly than revenue. Under those assumptions, Anthropic reaches $121.4 billion of revenue and an 11.4% adjusted operating margin in 2027, followed by $187.6 billion and a 17.9% margin in 2028. Gross margin would rise to 60% and then 63%.

    Those figures are a scenario, not an outcome you should build into a valuation without a stress test. They require revenue to more than double in 2027 while margins continue expanding. They also assume that efficiency gains outrun both competitive price pressure and the cost of training new frontier models.

    Use the eventual filing to answer six questions before deciding what the IPO is worth:

    1. Does profitability survive GAAP accounting? Start with GAAP operating income, then identify every adjustment. Stock-based compensation is an economic cost because it dilutes shareholders even when it does not consume cash in the period.
    2. Does profit convert into cash? Compare operating income with operating cash flow and free cash flow. Look for large changes in deferred revenue, payables, prepaid compute, and capitalized costs that could make accounting profit look stronger than cash generation.
    3. How binding are the compute commitments? A reported $1.25 billion monthly compute agreement associated with Colossus clusters, whose full cost was expected to begin appearing in the second half of 2026, is a major unverified input. Check the filing for duration, minimum-purchase terms, unused-capacity risk, and the ability to renegotiate.
    4. How durable is enterprise demand? Anthropic is estimated to have generated 78% of H1 2026 revenue from business customers. That is attractive only if renewals are strong and revenue is not concentrated among a few contracts. Look for customer concentration, net revenue retention, contract duration, and remaining performance obligations.
    5. Can pricing hold? Lower-cost open-weight models can pressure API prices and give large customers leverage in negotiations. Test whether future gross-margin expansion depends on lower compute cost alone or also assumes stable selling prices.
    6. What are you paying for the outcome? Calculate enterprise value using the offer price, fully diluted shares, debt, and cash. Compare it with trailing revenue, gross profit, GAAP operating results, and cash flow. Do not use the $69.7 billion monthly run rate as though it were audited annual revenue.

    The cleanest way to prepare is to save the current estimates as a provisional worksheet and replace them line by line when official disclosures arrive. Begin with GAAP income, stock compensation, cash flow, compute obligations, partner accounting, customer concentration, and dilution. Only then apply the offering valuation. Anthropic’s estimated profit turn justifies close attention, but no level of growth makes every IPO price attractive.

    References


  • Why I Run Each Prompt Once Daily: The Data Behind It

    Why I Run Each Prompt Once Daily: The Data Behind It

    I often get asked why I “only” run each prompt one time per day.

    For me, the answer comes down to signal quality. Running a prompt once daily gives me enough consistent data to understand performance without overloading the process with unnecessary repetition.

    The statistics show that a single daily run is plenty. It gives me a reliable view of how prompts behave over time, while keeping the workflow focused, efficient, and easier to interpret.


    Inspired by this post on Try Profound Blog.


    crushpress.ai community screenshot
  • Traffic Think Tank Joins Search Engine Land Community

    Traffic Think Tank Joins Search Engine Land Community

    [Boston, MA, July 6, 2026] — I am sharing that Traffic Think Tank has officially joined the Search Engine Land family, creating more opportunities for search marketers like us to connect, collaborate, and keep learning through one of the industry’s most established professional communities.

    I want members to know that Traffic Think Tank will continue operating as a private Slack community. It will remain a trusted place where we can exchange ideas, validate strategies, solve real marketing challenges, and stay current on search engine optimization, paid media, artificial intelligence, and related marketing topics.

    As part of this relationship, I see Search Engine Land supporting the community’s continued growth by increasing visibility across its editorial and marketing channels while preserving the collaborative environment members already value.

    “For years, Search Engine Land has represented the marketing community through its contributor network in a way few other sites have,” said Kyle Morley, Head of Sales and Marketing at Third Door Media, parent to Search Engine Land. “Launching a community like Traffic Think Tank feels like a natural extension of our identity, and I’m thrilled we now have more opportunity to connect with marketers in our space.”

    I am also noting that David Broderick has been appointed Lead Community Manager and will oversee the day-to-day community experience. He will be supported by Liz Dougherty, who will take an active role in encouraging member engagement and helping guide the community’s continued growth.

    Beyond ongoing peer-to-peer discussions, I expect members to benefit from expanded community programming and discussions, increased visibility through Search Engine Land and Third Door Media channels, exclusive discounts on Search Marketing Expo events and training, and new opportunities to connect with search marketers across the industry.

    For me, Traffic Think Tank fits naturally with Search Engine Land’s mission of helping marketers stay informed and succeed in a rapidly evolving search landscape. Together, the publication and community give us access to trusted journalism, practical education, live events, and an active peer network for ongoing professional development.

    Futuristic SEO and AI search illustration showing old tools breaking apart as blue data streams lead to a glowing search platform and digital icons.
    Old search marketing tools give way to a faster, connected future, with data streams, AI icons, and a glowing search hub symbolizing SEO innovation and community growth.

    If you are a search marketer interested in joining the community, I recommend learning more at https://searchengineland.com/trafficthinktank.

    About Search Engine Land

    I view Search Engine Land as a leading publication for news, insights, and education covering search engine optimization, paid media, artificial intelligence, and digital marketing. Through editorial coverage, events, training, and professional resources, Search Engine Land helps marketers stay ahead of industry change.

    About Traffic Think Tank

    I see Traffic Think Tank as a private community for search marketers that connects professionals through expert discussions, peer collaboration, and practical knowledge sharing. Members use the community to exchange ideas, solve challenges, validate strategies, and stay current on what’s working across search engine optimization, paid media, and artificial intelligence.


    Inspired by this post on Search Engine Land.


    crushpress.ai community screenshot
  • Why I Judge AI Deliverables by Outcomes, Not Effort

    Why I Judge AI Deliverables by Outcomes, Not Effort

    When I think about AI deliverables, I keep coming back to a simple scenario: a client receives two pieces of work.

    Both deliverables solve the problem they were hired to solve. Both are accurate, useful, and tied to the same business outcome. The client is happy, and from the outside, there is no meaningful difference in the results.

    Then the client learns that one took 20 hours to create, while the other took 20 minutes. That is when the uncomfortable questions begin.

    Was AI involved? Should the faster deliverable cost less? Is the person who completed it less skilled because they found a faster, more efficient way to reach the same result?

    What I find most interesting is how differently many of us react to AI depending on which side of the transaction we are on. I love using AI when it saves me time, but I also understand why customers can feel uneasy when they discover AI helped create something they paid for.

    I recently ran a LinkedIn poll asking a simple question: if the outcome is great, do we really care how it was made?

    The responses reinforced something I have been thinking about for a while. Many of the strongest objections people have to AI are not really about quality at all.

    The Time vs. Value Fallacy

    I think part of the discomfort comes from the fact that we have spent decades tying value to effort.

    Long hours feel valuable. Fast work feels suspicious. Struggle often gets mistaken for expertise.

    The harder something appears to be, the easier it becomes to justify the price attached to it.

    There is an old story about a ship engine that stopped working. After multiple failed attempts to repair it, the owners brought in an engineer with decades of experience. He inspected the engine, tapped it once with a small hammer, and the machine roared back to life.

    His invoice was $10,000.

    Image

    The owners were furious and demanded an itemized bill. The response was simple: hammer tap, $2. Knowing where to tap, $9,998.

    People debate whether that story is true or just a useful tale for people like me who believe in value-based pricing. But whether it really happened almost does not matter. The lesson still holds.

    People are not paying for the tap. They are paying for the expertise behind it.

    That is what makes AI such an important topic for me. It forces us to confront a question many of us have avoided for years: are we paying for expertise, or are we paying for visible effort?

    Those are not always the same thing.

    The Objections That Actually Matter

    To be clear, I do not think every objection to AI is unreasonable. I have shared plenty of my own concerns, and some of them are serious.

    In fact, I think the strongest arguments against AI have very little to do with how quickly something was created.

    Risk matters. Hallucinations matter. Bad recommendations matter. Compliance, privacy, and security concerns matter. Accountability matters.

    Those are legitimate concerns. What stands out to me is that none of them has much to do with how long it took to create the deliverable.

    They are questions of trust.

    Can the output be trusted? Can the recommendation be defended? Can someone confidently stand behind the work if it is questioned six months from now?

    ```json
{
  "alt": "SEO For Lunch Newsletter by Nick Leroy, featuring actionable SEO insights.",
  "caption": "Join Nick Leroy's SEO For Lunch: Your go-to source for actionable SEO insights served directly to your inbox.",
  "description": "This image promotes Nick Leroy's 'SEO For Lunch' newsletter, emphasizing actionable SEO insights. It features a smiling person against a dark blue background with the newsletter's branding, '#SEOFORLUNCH,' and website details. The design includes graphic elements like a fork and knife, alongside the tagline 'Not Your Average Table Talk.'"
}
```

    Because when something goes wrong, nobody gets to blame the AI. The employee is accountable. The consultant is accountable. The company is accountable.

    That is why I have always found the quality debate to be the least interesting part of the conversation. The more important question is not whether AI was involved. It is whether the outcome is trustworthy enough for someone to put their name behind it.

    The Outcome Test

    The more I think about AI, the less interested I become in whether it was used.

    Instead, I find myself asking a different set of questions. Was the outcome accurate? Was it useful? Was it better than the alternative? Would I be willing to stand behind it with my name, reputation, and credentials on the line?

    If the answer to all of those questions is yes, then I have a hard time arguing that the production method matters more than the result.

    I suspect this is where many people become uncomfortable because it shifts the conversation away from tools and back toward results.

    Ironically, this is also where humans become more important, not less.

    The future is not machines versus humans. I know, "The Terminator" and "I, Robot" movies will never feel the same. The real shift is humans using AI versus humans who refuse to adapt.

    The premium will not come from avoiding AI. It will come from judgment, taste, decision-making, communication, and accountability.

    AI can accelerate execution, but people still decide what should be built, what should be published, and what risks are acceptable. More importantly, people are still responsible for the outcome.

    The people who lose to AI will not be the ones using it. They will be the ones still evaluating effort while everyone else is measuring outcomes.

    This post first appeared on the author’s website and is republished here with permission.


    Inspired by this post on Search Engine Land.


    crushpress.ai community screenshot
  • Why I Stop Positioning AI as a People Replacement

    Why I Stop Positioning AI as a People Replacement

    I think one of the biggest mistakes in AI marketing is positioning a product as a replacement for people. That message can win attention in the short term, but I believe it quietly drains trust over time.

    This is a little different from what I usually write about, but it matters. The way we talk about AI shapes how customers, employees, executives, and markets respond to it.

    In this memo, I want to focus on three things: why “substitution positioning” feels powerful at first but weakens a brand later, what the data says about whether AI is actually replacing people, and how I think companies should position AI instead.

    Image

    The cardinal sin of positioning in the AI era is replacement. I call it substitution positioning. It is tempting because it sounds bold, efficient, and disruptive. But over time, it creates anxiety, skepticism, and credibility problems.

    We have seen this pattern already. Anthropic CEO Dario Amodei predicted that software engineering jobs could disappear within 6 to 12 months as models began doing most or all of what software engineers do end to end. Yet demand for software engineers has continued to look strong.

    Image

    OpenAI CEO Sam Altman also predicted that many customer support jobs would go away because AI could handle that work better. Soon after, customer service hiring began outpacing the broader job market.

    I understand why fear works as a marketing tool. The fear of being replaced gets attention fast. It got me, too. When powerful AI models gained traction, I worried about my own future. But when I still see AI companies hiring copywriters, SEOs, engineers, and support teams, I sleep better.

    Image

    Fear sells because it taps into fight-or-flight. Layoffs make that story even louder. They let companies frame cost-cutting as innovation and make the replacement narrative feel more real than it may actually be.

    But I do not think the facts support the clean replacement story. In New York, companies can indicate when mass layoffs are caused by technological innovation or automation. In one reported period, more than 160 companies filed mass layoffs affecting roughly 28,300 workers, and not one chose AI as the reason. That list included companies such as Amazon and Goldman Sachs.

    Image

    Researchers at Yale also studied employment data from the Current Population Survey over 33 months and found no evidence of job displacement from AI. To me, the pattern looks less like instant replacement and more like the earlier waves of computers and the internet changing how work gets done.

    That is why I keep coming back to this point: stop trying to make replacement happen. It is not happening in the simple, dramatic way many AI narratives suggest.

    Image

    AI is powerful, but it is also inconsistent. In its current form, it can do some tasks better than humans and fail badly at others. That paradox is often called the Jagged Frontier.

    The Jagged Frontier idea matters because it explains why some people see AI as transformative while others remain lukewarm. A BCG and Harvard study of 758 knowledge workers found that people get the most value from AI when they understand what it is good at and where it breaks down.

    Image

    Microsoft reached a similar conclusion in its 2026 Work Trend Index Annual Report. The company found that a small group of advanced AI users, described as Frontier Professionals, were not simply using AI more often. They also knew which mode of AI use fit each task.

    That distinction is important. The best AI users are not handing everything over blindly. They are applying judgment. They know when to use AI as a helper, when to use it as a collaborator, when to use agents for multi-step workflows, and when to keep a human firmly in control.

    Image

    I still do not trust most AI workflows enough to leave them running with no maintenance, review, or quality assurance. The question I ask is simple: would I bet my brand, customer experience, or revenue on a fully automated workflow with no human oversight?

    Klarna is a useful warning here. The company publicly promoted the idea that AI was doing the work of hundreds of agents and helping reduce headcount. Later, it reversed course and rehired humans after leadership acknowledged that aggressive cost-cutting had lowered quality and that customers still wanted a human option.

    Image

    That is the tradeoff I see with substitution positioning. It creates immediate attention, but it can damage long-term credibility. The words often do not match the operational reality.

    Replacement positioning could work if customers truly wanted full replacement and if the technology were consistently ready for it. I do not think either condition is true.

    Image

    Cost reduction is a strong AI argument because it shows up quickly on the P&L. Productivity gains usually take longer. They build inside companies over time and often take even longer to appear across the broader economy.

    But when replacement positioning goes beyond cost-cutting and becomes people-cutting, I believe it starts to antagonize the very people companies need to win over.

    Image

    We have already seen backlash. Duolingo’s AI-first memo drew heavy criticism before the company reframed AI as a tool to accelerate work rather than replace contractors. Surveys have found that some workers refuse to use AI tools because they fear job loss. Pew has reported that many U.S. adults are more concerned than excited about AI in daily life. Reuters/Ipsos polling has shown widespread fear that AI will permanently displace workers.

    There is also a quality problem. When employees believe the purpose of AI is to replace them, they may disengage or produce lower-quality work. In my view, that is not just an adoption issue. It is a positioning failure.

    Image

    Executives often feel more excited about AI than the employees asked to use it every day. That gap matters. If leadership talks about AI as a replacement engine, employees hear a threat. If leadership talks about AI as leverage, employees have a reason to learn.

    Token economics also complicate the replacement story. Some companies have bragged about massive AI usage, but token costs are still a real business variable. As those costs normalize, the math may make junior employees look interesting again, especially when human judgment, context, and accountability are part of the output.

    So what should replace replacement? I think the answer is enhancement. Instead of positioning AI as a way to remove people, I would position it as a way to make capable people more effective.

    AI can be used in two broad ways. A company can try to reduce the number of people, or it can grow output with the same number of people. The data I have seen suggests that productivity gains often create the stronger return.

    A National Bureau of Economic Research paper surveyed 750 executives about AI’s impact on productivity and labor markets. Larger firms showed more interest in replacing labor costs, but the highest ROI came from productivity growth.

    That is the lesson I take from the research: doing more with the talent you already have is often stronger than trying to remove the talent that knows what good work looks like.

    Building products has become easier, but distribution has not. When supply explodes, the scarce thing is not output. The scarce thing is being the product, brand, or service that actually gets chosen.

    That is why positioning matters more than ever. Product quality still matters, but the way I frame AI use can determine whether people see it as empowering or threatening.

    My takeaway is simple: I would stop selling AI as a people replacement. I would sell it as judgment leverage, workflow acceleration, and creative expansion. Fear can get attention, but empowerment is a better long-term strategy.

    This post first appeared on the author’s website and is republished here with permission.


    Inspired by this post on Search Engine Land.


    crushpress.ai community screenshot
  • Discover Google’s New Search Profiles for Publishers

    Discover Google’s New Search Profiles for Publishers

    Hey there, have you heard about Google’s latest feature within Google Discover? They’ve just launched Search profiles in the U.S., and it’s a game-changer for publishers like me. These profiles act as enhanced landing pages where my audience can not only follow me but also see a collection of my latest articles, videos, and social media posts all in one convenient spot.

    Google has been working on this for quite some time, refining and testing it over several months. They’ve even made some tweaks, such as adding shortnames, which make it even easier to share these profiles.

    What are Search Profiles? According to Google’s description:

    “Search profiles give publishers and creators a central place to showcase their latest articles, videos, and social posts. People can easily follow sources from their profile, so they’re more likely to see that content on Discover, found on the home screen of the Google app.”

    It’s described as a “new way for publishers and creators to shape their presence on Search. Search profiles are a dedicated, shareable space to highlight content across platforms and help audiences find accurate, up-to-date information about sources on Search.”

    What it looks like: Curious to see it in action? Here’s a video demonstration:

    Managing Your Search Profile: If you’re a publisher or creator with a significant following on a major social or video platform, you’re in luck! You’ll be able to claim your Search profile, personalize it with an avatar, bio, and links to your website and social media platforms.

    Once you claim your profile, it might even create a Knowledge Panel for you, or enhance your existing one with updated details and a direct link to your profile.

    If you’re interested in setting up your own Search profile, check out this guide for creating a profile, claiming an existing one, and managing it.

    Availability: Currently, this feature is available in the U.S. for users and publishers who meet a certain follower threshold. Here’s what you need:

    • TikTok: 300,000 followers
    • YouTube: 100,000 subscribers
    • Instagram: 100,000 followers
    • X: 100,000 followers

    Why This Matters: As a publisher, I’m always looking for ways to get more visibility. Google’s new feature allows us to increase our reach not just on Google platforms but across our entire digital presence. It’s an exciting time, though one has to ponder whether this will be enough in the fast-paced world where AI continues to evolve.


    Inspired by this post on Search Engine Land.


    crushpress.ai community screenshot
  • Unveiling Google Search Console’s AI Controls and Reports

    Unveiling Google Search Console’s AI Controls and Reports

    As someone who eagerly follows Google’s updates, I was thrilled to learn about the latest developments in Google Search Console. Recently, Google has started to roll out new Search Generative AI performance reports. These reports, along with a feature to block your content in AI responses, are designed to give website owners more control.

    Currently, these features are being introduced to a select group of website owners in the UK, but there are plans to expand access in the near future. This gradual rollout allows us to get accustomed to these changes before they become widely available.

    Exploring the Search Generative AI Performance Report

    The new AI performance report in Google Search Console is something I’ve been anticipating. Although it doesn’t cover everything, it does provide some important insights into how our content is performing within AI responses, AI Mode, and AI Overviews on Google Search. The report includes data on impressions, pages, countries, devices, and dates. However, a notable omission is click data, so we’re left guessing about the exact number of searchers clicking through to our sites from AI responses.

    Google stated:

    – We’re rolling out new insights for website owners regarding their pages’ appearances in generative AI Search features. These insights include impressions metrics and information on which pages appear in AI responses and in which countries. We’re working closely with website owners to determine what insights would be most helpful and will expand the metrics available over time. 

    Additionally, Google shared more details about the metrics we can expect:

    – Impressions: Frequency of your site’s URLs appearing in generative AI features in Search and Discover.

    – Pages: Identifying URLs that appeared within AI features.

    – Countries: Understanding visibility on a country basis.

    – Devices: Identifying the devices used to view your website. Available for Search results.

    – Dates: Monitoring performance with hourly, daily, weekly, and monthly granularity.

    I inquired about click data from a Google representative, who mentioned that they are exploring additional metrics that will help inform our strategies in the future.

    Initially, this report is available to a subset of users in the UK, with plans to expand globally in the future.

    If you want to explore more about this report, I recommend checking out the Google help center document.

    Introducing AI Blocking Controls

    Another exciting feature Google introduced is the ability to block your content from appearing in AI search features like AI Overviews, AI Mode, or AI Discover. Google described this as a “new toggle” within Google Search Console, allowing us to decide whether or not our site should be part of these AI search features.

    Google notes that opting out will prevent your site from receiving traffic or impressions from these features. Importantly, this control won’t affect your ranking in standard search results outside of generative AI Search features, so there’s no risk of negatively impacting core web search visibility.

    Again, like the performance report, this toggle is currently available to a subset of UK website owners, with plans to widen access as they complete further testing. Google had promised these controls after facing some backlash from the EU, and it’s promising to see them starting to roll out now.

    One study even showed that 1/3rd of SEOs are willing to block Google from showcasing their content in AI search features.

    Why It Matters

    As site owners and publishers, many of us have been asking for control over how and if our content appears in Google’s AI features. Now, we have just that. Although it’s initially limited, I’m hopeful these features will eventually be available to all.

    Moreover, we’ve been requesting AI Search reporting from Google from day one. With Google’s announcement following Bing’s release of its own AI performance report, we’re taking a significant step forward. While Google’s report currently targets UK site owners and lacks click data, it holds promise for a global rollout soon.


    Inspired by this post on Search Engine Land.


    crushpress.ai community screenshot
  • Why ‘It’s Just SEO’ is Limiting Our Industry’s Growth

    Why ‘It’s Just SEO’ is Limiting Our Industry’s Growth

    I’ll be honest; the ongoing discourse around the GEO debate feels like a distraction from a much more significant transformation. AI systems are reimagining how brands, sources, and recommendations are surfaced, demanding our full attention.

    It’s both impressive and frustrating how search has managed to spark such passionate debate at a time when it should be becoming more pivotal to clients. Yet, our industry is stuck in arguments that render us irrelevant.

    So, who truly owns the future of search? That’s the real question we need to tackle.

    Who defines the next phase of search? Who secures the budget? Who articulates the shift from a list of links to a machine-driven recommendation system?

    The phrase “it’s just SEO” has caused considerable damage. It sounds like the calm, seasoned wisdom you’d expect from a search veteran. However, it lacks strategic depth. It’s a meme that constrains one of the most substantial commercial opportunities in years.

    Why Memes Matter in Search

    Memetics isn’t a new concept. Richard Dawkins introduced it in “The Selfish Gene” in 1976, suggesting that ideas spread through culture in a fashion similar to genes. Susan Blackmore expanded on this, claiming we’re essentially ‘meme machines’ built to propagate cultural information. The most resilient ideas aren’t necessarily true; they’re the stickiest.

    Take “Happy Birthday to You,” it’s memorable and universally known not because it’s brilliant, but because it’s easy to replicate and emotionally fulfilling. Slogans and professional clichés endure for their simplicity and utility, not their accuracy.

    SEO and GEO are entangled in a memetic struggle. This issue is amplified as the phrase “it’s just SEO” became predominant when GEO appeared, driving a wedge into meaningful conversation.

    When GEO first came into the discussion, reactions varied. While some recognized the need for new tools and methods, others viewed it as a threat, repelling it with the phrase “it’s just SEO” — turning it into a chant and then a weapon. It was an ideal meme, short and socially protective.

    The follow-up meme “GEO grifter” did even more harm, framing advocates of GEO as opportunists and stifling exploration and innovation. This behavior causes harm when consensus forms based solely on repetition, with the algorithms rewarding those repeating the framing, creating a false sense of agreement.

    Clients Seek Certainty, Not Acronyms

    I’ve observed firsthand at conferences like BrightonSEO that many marketers are already leveraging generative systems. They don’t need debates over terms; they’ve adapted to new processes accordingly.

    SEO has always been difficult to sell against paid counterparts due to previous uncertainties and failures. Nonetheless, good SEO generates tangible success. Failing to clarify the changes will see budgets drift elsewhere, especially to paid avenues.

    ```json
{
  "alt": "The CapmatchOne logo with a gradient circle and bold text.",
  "caption": "Discover innovation with the CapmatchOne logo, featuring sleek typography and a modern gradient circle.",
  "description": "The CapmatchOne logo features bold, modern typography coupled with a gradient circle, symbolizing connection and innovation. The sleek design conveys a sense of progress and creativity. This image can be used for branding or promotional purposes, appealing to audiences interested in innovative solutions and forward-thinking designs."
}
```

    The B2B Institute’s Findings

    According to LinkedIn’s B2B Institute, growth for B2B brands stems from being easy to locate. Digital environments now demand visibility across new platforms.

    The report views GEO as an extension of SEO and emphasizes establishing authority, relevance, and credibility. Discoverability is altering, yet core principles endure.

    The 9 a.m. to 5 p.m. Dilemma

    “It’s just SEO” oversimplifies a vast concept. When someone insists GEO is “just SEO,” I must ask — which kind? Each interpretation involves different practices and focuses.

    If our response to generative systems is “helpful content,” we’re on the wrong track. The future demands more than vague promises; it requires adopting digital PR, brand strategies, and tactical marketing insights.

    No Name, No Funding

    Markets can’t invest in what they don’t recognize. Naming GEO is crucial as it turns abstract threats into actionable categories. Without a name and a defined category, the industry will fail to secure the investments needed to thrive in an altered landscape.

    Ultimately, whether we call it GEO, AI search visibility, or SEO evolved, defining it ensures survival and growth. Brands that embrace this will capture opportunities that arise as search evolves.

    A New Framing for Change

    It’s time to acknowledge change and redefine the narrative. The transformation involves becoming the recommended brand — present, visible, and credible. It’s about expanding SEO to embrace the broader spectrum of digital marketing.

    Adapting to these shifts will ensure brands maintain their visibility as search continues to evolve. Those clinging to outdated debates are at risk of missing out entirely.


    Inspired by this post on Search Engine Land.


    crushpress.ai community screenshot
  • Discover Google Chrome Lighthouse’s New AI Scan Feature

    Discover Google Chrome Lighthouse’s New AI Scan Feature

    I’ve recently discovered that Google has introduced a new feature in Chrome Lighthouse to check for llms.txt files. Though Google mentions that llms.txt isn’t necessary for AI search visibility, Lighthouse has started flagging sites based on their presence.

    Google’s latest Lighthouse audits, under the “Agentic Browsing” category, now focus on a site’s usability for machine interaction. I find this interesting as it aligns with Google’s push towards better machine readability.

    The new audits are part of Chrome’s evolving “Agentic Browsing” features, which analyze if sites are prepared for automated interaction. This concept came soon after Google issued guidance on AI search optimization, debunking the necessity of llms.txt files in their new guide on generative AI features.

    What Lighthouse Evaluates Now. Lighthouse’s Agentic Browsing tests focus on how well my site is built for machine interactions, incorporating various deterministic audits as per Google’s documentation. These checks include:

    – WebMCP integration.

    – Accessibility tree integrity.

    – Layout stability through CLS.

    – Presence of an llms.txt file.

    These audits help ensure that there’s a machine-readable summary at the site’s domain root. Google explains that without llms.txt, agents might take longer to understand a site’s main structure.

    The impact of these audits doesn’t translate into a traditional Lighthouse score but into a fractional pass ratio related to agentic readiness signals.

    The Tension. Interestingly, while these audits don’t directly affect SEO rankings, their mention in Google’s readiness checks could make SEOs reconsider their stance on llms.txt files.

    Agentic Engine Optimization. Google’s approach aligns with insights shared by Addy Osmani from Google Cloud AI about Agentic Engine Optimization. Osmani emphasizes creating web content that is semantically structured, token-efficient, and easy for AI to process.

    SEO vs. llms.txt. According to Google, creating llms.txt or similar files isn’t necessary for AI search success, as outlined in the guide on Mythbusting generative AI search. The AI systems can discover, crawl, and index a variety of file types encountered on the internet.

    John Mueller from Google responded to concerns about the role of llms.txt in a discussion with Lily Ray on Bluesky, stating that the use of these files is more for functionality and not directly linked to search engine optimization.

    Google’s Take on AI Agents. Besides llms.txt, Google’s Lighthouse guidelines place strong emphasis on accessibility and interface stability. The insight I gained is that AI agents heavily rely on the accessibility tree as their core data model, focusing on integrity and proper layout.

    Ultimately, while Google indicates llms.txt isn’t needed for search, including such files might be beneficial for adapting to Google’s evolving tools that prioritize machine readability.

    Further Exploration.

    – Meet llms.txt, a proposed standard for AI website content crawling

    – llms.txt isn’t robots.txt: It’s a treasure map for AI

    – Does llms.txt matter? We tracked 10 sites to find out


    Inspired by this post on Search Engine Land.


    crushpress.ai community screenshot