A few days ago, I was at an investor conference focused almost entirely on consumer packaged goods: cereal, snacks, beverages, household staples. Traditional CPG.

And yet one of the dominant conversations wasn’t about pricing, supply chains, or retail expansion. It was AI!

More specifically, it was about how large language models are beginning to influence what consumers buy. One stat stood out above everything else: roughly 25% of consumers are already using AI tools to get recommendations for products they can purchase in stores. That number is likely headed much higher over the next few years.

That changes everything. For decades, brands fought to win at the shelf. Then they fought to win in search. Now they have to win in chat.

A Short History of Shopping

In the 1990s, shopping was physical. Consumers walked aisles and made decisions based on packaging, placement, and price.

In the 2000s, the internet began shaping decisions before shoppers entered the store. Search engines and retailer websites became influential.

In the 2010s, smartphones compressed the decision window even further. Consumers researched products in real time, often while standing in the aisle.

Now we’ve entered the next phase: conversational commerce.

Consumers increasingly skip search entirely and ask AI directly:

• “What’s the healthiest cereal?”

• “What’s a better alternative to this snack?”

• “Which pasta sauce has the cleanest ingredients?”

Instead of ten blue links, they get one synthesized answer.

That means the interface for discovery is no longer the shelf or the search engine.

It’s the answer layer.

1. Turn Your Website Into an Answer Engine

Most brand websites were designed for human browsing.

Now they also need to be readable by AI systems.

Brands should clearly explain:

• what a product is

• who it’s for

• why it’s different

• how it compares to alternatives

The clearer the explanation, the more likely it is to appear in AI-generated recommendations.

2. Structure Content Around Questions

Consumers interact with AI conversationally, not through keywords.

That means brands should create content that directly answers questions people actually ask:

• Is this healthy?

• Is it worth the price?

• What’s the best option for kids?

• How does this compare to competitors?

Brands that mirror natural language queries are more likely to surface in AI outputs.

3. Own the Comparison Narrative

If brands don’t explain how their products compare to competitors, AI models will do it for them.

That’s risky. The winning brands will proactively publish:

• comparisons

• rankings

• “best for” use cases

• nutritional breakdowns

• feature differentiation

In the LLM era, comparison content becomes strategic infrastructure. If your gun shy on having your competitors ranked on your own site, then you might as well give up now. You won’t win in the new world of Answer Engines!

4. Build Strong AI-Readable Entities

AI models don’t just recognize keywords. They build associations.The strongest brands consistently reinforce the attributes they want associated with their products:

• low sugar

• high protein

• premium ingredients

• affordable

• sustainable

• family-friendly

Over time, these associations shape how models describe and recommend products.

5. Optimize for Zero-Click Decisions

In many cases, the consumer will never visit your website.

The AI summary is the customer experience. That means your brand messaging needs to survive compression into a few sentences inside ChatGPT, Gemini, Claude, or other systems.

Brands now need to think not just about what humans read, but what machines summarize.

The Bottom Line

We are entering a world where a growing percentage of purchase decisions happen before a consumer reaches the aisle. That is a fundamental shift.

The brands that adapt earliest, by structuring information clearly, shaping AI-readable narratives, and optimizing for conversational discovery, will become the default recommendations inside the systems consumers increasingly trust.

For decades, brands competed for shelf space. Now they’re competing for answers.

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One of the reasons people got excited about platforms like OpenClaw is simple, it feels local.

What that means in practice is powerful. Instead of uploading your data and credentials to some distant AI service, you can keep everything on your own machine, your Gmail, Slack, Google Drive, Salesforce, even Apple services, all logged in locally. Then, you layer an AI agent on top to automate workflows, whether that’s scanning emails, triggering actions, or coordinating tasks across systems.

At first glance, this seems like the perfect balance, local control of sensitive data and credentials combined with powerful AI-driven automation. That’s exactly why tools like OpenClaw captured so much attention.

The Illusion of “Local” AI

But there’s a catch, and it’s an important one.

While platforms like OpenClaw operate locally in terms of access and orchestration, the intelligence itself is usually not local.

To actually perform meaningful reasoning, these systems still rely on external large language models (LLMs), such as those from OpenAI, Anthropic, or others. In practice, that means data is often sent to external servers, computation happens off-device, and results are returned back to your local environment.

So while your logins may stay local, your data flow does not fully stay local.

This creates a real concern: If your data still needs to leave your machine for intelligence, is it truly secure?

That tension, between local control and remote intelligence, is where the current generation of tools falls short.

A Look Back, When Software Was Truly Local

To understand where this could go, it helps to look back.

In the 80s, 90s, and early 2000s, software was fundamentally different. You installed tools like Microsoft Office directly onto your machine, and everything, from processing to storage, happened locally, with no reliance on the cloud or external services.

That model gave users full control over their data, eliminated the need for constant connectivity, and created a predictable, contained environment.

AI today has largely moved in the opposite direction, toward centralized, cloud-based intelligence, but that may not be where it ends.

The Shift Toward Fully Local AI

What’s emerging now is the possibility of true local AI, where both the data and the computation stay entirely on-device.

This shift is becoming more realistic thanks to smaller and more efficient models, advances in hardware like GPUs and Apple Silicon, and optimization techniques such as quantization and distillation. Together, these make it possible to run increasingly capable models locally without constant reliance on external infrastructure.

In this world, you wouldn’t need to send your data to any external LLM at all. Your AI assistant would run locally, process locally, and operate within your environment, meaning your data never leaves your machine.

Why This Matters, The Enterprise and Proprietary Data Use Case

The biggest impact of local AI won’t be consumer convenience, it will be enterprise trust.

Take a hospital, for example. Imagine decades of proprietary radiology reports, tens of thousands of highly sensitive records. Today, even with modern AI tools, there’s hesitation around whether any of that data is being sent externally, whether it could be used for training, and whether it fully complies with privacy regulations.

Even with safeguards, the concern remains.

Now imagine a different scenario, a fully local AI system that is pre-trained but deployed entirely on-premise, capable of analyzing those radiology reports without ever transmitting data outside the organization.

That changes everything.

The same dynamic applies across financial institutions, defense systems, legal firms, and any environment where proprietary or sensitive data is core to the business. In these cases, data privacy isn’t just a feature, it is the product.

Where the Real Competitive Moat Will Be

Today, many companies are building on top of LLMs, creating wrappers, agents, and workflow tools.

But LLM capabilities are rapidly converging. Open and commercial models are getting closer in performance, and switching between them is becoming easier. As a result, the long-term advantage is less likely to come from simply having the best model.

Instead, the real moat may come from the ability to run AI securely, locally, and effectively on proprietary data.

That’s a very different kind of advantage, and a much harder one to replicate.

The Next Wave, Local AI Appliances and Preconfigured Systems

Don’t be surprised if the next evolution looks something like this.

Instead of subscribing to a cloud service, you might purchase a pre-configured local AI system, perhaps costing $1,000 or more depending on its capabilities, designed for a specific use case such as healthcare analysis, enterprise workflows, or personal productivity.

You install it, or it arrives as a dedicated device, and it runs entirely locally. It’s optimized for your workflows, configured for your environment, and critically, it never sends your data back to any provider.

In many ways, this would mark a return to a familiar model, software you own, running on machines you control, now enhanced by AI.

Why Platforms Like OpenClaw Still Matter

This is where platforms like OpenClaw become especially important.

They represent an early step toward local orchestration, credential control, and workflow automation tied directly to real user environments. Even if they still depend on external models today, they serve as a bridge between today’s cloud-based AI and a future where everything runs locally.

As local models improve and hardware continues to advance, platforms like these could evolve into fully self-contained AI environments with no external dependencies.

Final Thought

We are in a transitional phase. Right now, AI is powerful, but mostly centralized. Tools like OpenClaw give us a glimpse of a more controlled, local-first future, but they haven’t fully crossed that boundary yet.

When true local AI arrives, where intelligence, data, and execution all live on your machine, it won’t just improve these platforms. It will redefine their value entirely.

And that’s when tools like OpenClaw, and everything built around them, become not just useful, but essential.

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How to Unlock Your Generative Engine Optimization (GEO) and Answer Engine Optimization (AEO).

At Literate AI, we’ve been in the AI trenches since before ChatGPT launched, getting nerdy on how semantic search and Natural Language Processing (NLP) make or break brands and reputations. Earlier, our founding team of Mitch Stoller, Matt Van Dusen and Gabe Gold launched and led Group SJR, the first pure-play content marketing agency.

So we have a strong view of what makes content work, how to get discovered in search, build a consumer/customer journey across your site, social, and more. But with the rise of the Chatbots everything’s changed. It’s no longer enough to think about what humans want. You need to understand the Bot’s Journey, too.

As a leader in AIO, GEO and AEO spaces—well at least the NYT, the Ankler, Sports Business Journal have quoted us and published our research—we wanted to share our principles for success. We’ve derived these rules from applying our geekiness to helping global enterprises, tech startups, governments and NGOs get discovered by Generative AI.

Here’s our Ten Laws of Generative Engine Optimization (GEO) and Answer Engine Optimization (AEO).

Law 1: AI is Your Brand’s Most Important Audience

Stop worrying about your human customers for a second. In a zero-click world, AI answers questions, not Search Engine Results Pages. It means you need to win over the Bots!

Law 2: Search is Snobby, AI is Hungry

The rise of AI Overviews and answers also means the reign of search snobbism is over. In the past, if you weren’t legacy media, you were sitting at the kid’s table. But AI is different. It’s hungry for deep, specific information to answer the next five follow-up questions a user might ask.

Law 3: Machines Don’t Read Like We Do

Maybe you think the robots are reading your blog posts. Wrong. They are doing math, and they don’t care about a hook that pulls you in. They break your words into ‘tokens’ and turn them into numerical IDs. It’s all vectors and probability.

Law 4: Write for Robots, Not Just People

Keep your storytelling for Substack. You need to be a direct answer machine. Stop flirting with the reader and start answering their follow-up question. Structure your content for the human eye, and you’re invisible to the algorithm. Structure it for the neural network, and you become the answer.

Law 5: Algorithms Are a Moving Target

Back in the Golden Age of SEO, Google rewarded great content with high search engine results page rank. Great blue links! Not anymore of course. But we’re in a similar Golden Moment for AI right now, but it won’t last. LLMs will eventually stop ingesting content for free and start charging you for discovery. If you aren’t already the ‘right answer’ when that shift happens, breaking into a chatbot response will be just as hard as landing page one on Google^.

Law 6: Know Thyself to Know Thy Queries

The machines are black boxes. They won’t tell you what people are asking. So, if you want the right answers, you have to find the right questions yourself.

Forget keyword volume. You need to map your own ‘Query List’—40 to 60 deep questions that define your brand and your category. This is how you discover what the AI doesn’t know about you. If you don’t shape the questions now, you’ll never control the answers later.

Law 7: Your Website is Your Most Valuable Digital Asset

Think your website is for customers? Think again. In the age of AI, your website is actually a training manual for the robots. Stop obsessing over ‘user journeys’ and start building ‘bot journeys. Use your owned digital property to feed the LLMs. Structure your sentences with positive sentiment and isolate the negatives. If you aren’t providing the data, the AI will just guess

Law 8: Map the Bot’s Journey

Marketing since 1898 has been about AIDA: Attention, Interest, Desire, and Action. But here’s the truth: Chatbots don’t have desires. They have algorithms. The consumer journey is dead. You need to map the Bot’s Journey. It’s not about emotional persuasion; it’s about predictive patterns and data sequences.

To win, you have to get RADD: Relevance, Authority, Discovery, and Depth. The bot is looking for HTML over PDFs and depth over catchy headlines. If you want to be the answer, you have to understand where the bot looks and in what order. If you aren’t RADD, you’re forgotten.

Law 9: Winner Won’t Take All

For 20 years, Google was the only game in town. But in the AI age, the ‘Winner Take All’ world is officially over. Google is cannibalizing its own search business just to keep up, while Perplexity and Claude are carving out massive territories in research and code.

You can no longer rely on a single silo. Even Grokipedia is rising to challenge Wikipedia’s dominance over AI training data.

Your brand needs to be everywhere the bots are. If you’re only optimized for one channel, you’re missing the rest of the world. Stop betting on one horse.

Law 10: Orchestrate, Don’t Dictate

The final law to rule them all: Orchestrate, don’t dictate. Marketing in the AI age isn’t about managing silos; it’s about leading a symphony.

Forget simple automation. Orchestration means using AI to sync every customer touchpoint in real-time—from SMS to LLM answers.

Automation is doing things right. Orchestration is doing the right thing at the exact moment your customer needs it.

McKinsey projects this market will hit $1 trillion by 2030. If you don’t start thinking holistically today, you’ll be left behind tomorrow.

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I’ve been spending a lot of time lately experimenting with different AI coding and agent-style tools, and as an engineer who actually uses this stuff day to day, I tend to care less about hype and more about what holds up after a few real workflows.

OpenClaw was one of those projects that caught my attention early because it showed something important: you can start to stitch together a more autonomous, local-first development experience. That said, after putting OpenClaw through its paces, I found myself reaching for a handful of other tools more consistently. Here are six practical replacements for OpenClaw available now, each with a different twist.

Perplexity Computer is the closest thing to a thinking layer in the cloud that directly takes on OpenClaw. It’s strong at breaking down problems, comparing strategies, and helping you discuss decisions before you even touch code. It ends up being more useful than expected because it reduces bad starts, which is something most AI Agent tools don’t address. One of the biggest gripes from OpenClaw users is bad starts. Users have to spend hours setting OpenClaw up and then even more time setting up the right work flow.

Tasklet.ai is more execution-focused. It’s not trying to be everything. It’s trying to help you actually get repetitive or structured tasks done without spinning up full systems. When I want to chain together small workflows or automate parts of a process that are too minor to justify building out but too frequent to ignore, it comes in handy. It sits nicely in that middle ground between scripting and full automation. Tasklet recently got $20m in funding from leading VCs; so a lot of smart people see a future for this company.

Base44 has an experimental edge, but it’s still productive. It’s the kind of tool you reach for when simple prompt-in, answer-out starts to feel limiting. It gives you more flexibility to shape interactions and build more complex flows without a ton of overhead. It’s not the most polished, but it’s definitely one of the more interesting OpenClaw alternatives if you like pushing boundaries.

Claude Code is one of the more dependable tools in my workflow. It handles larger context better than most, which matters when you’re working across multiple files or trying to maintain consistency. When I need actual reasoning and not just suggestions, especially for refactoring or understanding unfamiliar code, I use it. It’s not flashy, but it’s consistently useful. But one challenge with Claude Code vis-a-vis OpenClaw is that it’s designed primarily for engineers, making it hard to setup workflows .

Cursor AI is where things start to feel embedded into your actual development environment. Instead of stepping out to ask questions, it works alongside you while you code. It understands the context of your project as you go, which changes how you write and iterate. It’s less about one-off help and more about continuous assistance, which makes a noticeable difference over time. Again, a bit heavy on the coding side, but you can set up workflows that are more complex - think enterprise level workflows in complex industries.

GitHub Copilot is still the baseline tool that just works. It’s widely integrated, low friction, and reliable for everyday coding. Even if some of the newer tools are more advanced in certain areas, Copilot is often the one that keeps you moving without thinking about it. It’s not trying to redefine your workflow, it just quietly speeds it up.

The Playing Field

If we take a step back, OpenClaw deserves credit because it proved autonomous, local development is possible and useful, especially for developers who care about control and local execution. However, these six and dozens of other platforms coming online will take the local AI movement to the next level.

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By Mitch Stoller, Chairman of Literate AI

For fifty years, we lived in a world of “snobby” search. If you wanted to be discovered, you had to bow to the high priests of PageRank—the legacy media outlets like the New York Times or the Wall Street Journal. We spent our lives chasing the 2.6% of citations that come from Tier 1 publications, hoping a “blue link” would save our brand.

That world is gone. We are now in a “Zero Click” reality where an estimated 60% of searches end inside an AI-generated answer. Today, your most important audience isn’t a distracted consumer or a cynical editor—it’s a predictive algorithm.

As I’ve said before, Search is Snobby, but AI is Hungry. These Large Language Models (LLMs) are chomping at the bit for deep, technical, and specific information to answer the increasingly granular questions of your customers. To win in this environment, you need to stop writing for the “hook” and start writing for the “robots”.

That’s why we developed the R.A.D.D. framework. It’s the playbook for any brand or leader who wants to stop being invisible and start being the authority.

Relevance: Aligning with the Vector

Machines don’t read like we do. They break text into tokens and analyze mathematical patterns. To be Relevant, your content must align with the specific word patterns and “vectors” used by predictive algorithms.

Stop burying the lead. If you want to be the answer to a question about “portfolio risk in a volatile market,” you need to use the specific terms and positive sentiment associated with that solution immediately. If the machine can’t find a clear pattern, it will move on to someone else who made their points more clearly.

Authority: Owning the Owned

Your boring corporate website is now your most valuable digital asset. Why? Because you own it. In the age of AI, the machines look for sites with demonstrated authority based on links and depth.

Don’t wait for a journalist to validate you. By publishing high-quality, detailed content on your own domain, you establish yourself as the “Mount Olympus” for your specific niche. If you provide the most authoritative data, the LLMs will have no choice but to cite you.

Discovery: Optimizing the Bot’s Journey

The bots follow a specific journey: Relevance, Authority, Discovery, and Depth. To ensure Discovery, you must optimize your technical infrastructure.

LLMs prefer clean HTML over clunky PDFs. They need to find your pages easily to index them. If your “raw ore” is locked away in a format the machine can’t parse, you are effectively invisible to the very engines that are telling people what matters.

Depth: Answering the Next Question

Journalism is often shallow because it needs a click. AI is different; it wants to answer the follow-up. To achieve Depth, you must map out the 40 to 60 questions that define your category and answer every single one of them with meaningful detail.

If you provide a shallow answer, the user (and the bot) will look elsewhere. If you provide the depth, you stay in the “answer loop” and guide the consumer through their entire research journey.

The Golden Moment

We are in a golden window for Artificial Intelligence Optimization (AIO). The algorithms are still learning, and they are hungry for your expertise. But this window will close as platforms integrate advertising and become more selective.

Don’t just automate—Orchestrate. Use R.A.D.D. to turn your brand into the definitive source of truth. The robots are reading. Make sure they’re reading you.

RADD Scorecard

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To establish yourself as a category authority in the “Zero-Click” world, you must evaluate your content through the eyes of ChatGPT, Gemini, Perplexity, and the other LLMs. This R.A.D.D. Scorecard allows you to audit your existing articles and determine if they are feeding the hungry AI or simply whistling in the digital wind.

Welcome back to the 10 laws of AI Optimization.

Today we lay out our final law–the law to rule all the others. The foundation of marketing and companies in the AI age.

Orchestrate, don’t dictate.

Let’s explain what this means in two parts.

First, stop thinking in silos. That’s the big change we talked about in our last law–the Winner Take All Age is Over.

Here’s why. We have too many silos. Way too many. With different specialists handling each one.

Meta

AdWords

SEO

Content

Email

Comms

SMS

Linear TV

Streaming TV

YouTube

TikTok

AEO

Ecomm

Amazon

Walmart

Influencer

Affiliate

Who knows what else.

It’s just too much, too many different disciplines. Each is best for certain campaigns, messages and products. Each filled with subtilities and nuances. Each has a good or even great manager.

It’s very, very hard to manage across all these lanes.

Yet that’s about to change.

With the right AI tools, you can use an existing platform or build your own on top of an LLM, you will be able to coordinate across all these different channels.

Real-time, data-driven, syncing of customer actions across all your touchpoints.

Done right, you will be able to create a cohesive, personalized journey.

We’re not talking about marketing automation–the efficient execution of linear tasks. Like a series of emails.

Orchestration will be dynamic, context aware and cover all the bases.

Automation is doing things right.

Orchestration is concerned with doing the right thing at the right time for the right customer.

It’s the ultimate skill in AIO. True, it’s just in its early days. Heck, so is AIO.

But now is the time to start thinking and learning about where to start in AIO–making sure LLMs get your right–to where you can truly take. Orchestrated AI-driven brand and reputation-building and commerce.

McKinsey projects the B2C market alone could reach $1 trillion by 2030. That’s just in the US.

AIO demands a different kind of thinking–a holistic point of view matched with a high level of experimentation and flexibility.

Nothing sums it up better than our final rule.

Orchestrate, don’t dictate.

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I’m consistently surprised by how few clients have even considered the possibility of selling their products or services through ChatGPT.

Most are only just catching up to the idea that their customers are using it to research and compare providers. That alone still feels new. Teams are beginning to ask how they show up, how they influence answers, how they avoid being invisible.

But the next step rarely comes up. Why?

• No clear owner - It cuts across SEO, content, and sales, so no team takes responsibility.

• No familiar metrics - No traffic, no attribution, no dashboard, so it feels invisible to leadership.

• It requires mastering a new channel - New behaviours, new mechanics, and no established playbook.

• It disrupts what already works - Investing here forces uncomfortable questions about paid search, websites, and existing funnels.

So, what happens when that same interface moves from recommendation to transaction?

We are not fully there yet. The merchant infrastructure is early. The rollout is uneven. Most businesses cannot plug in overnight and start generating revenue.

But the direction is clear.

OpenAI has already signalled where this is heading. ChatGPT is evolving from a discovery layer into something closer to a full commercial environment. Consumers are already comfortable asking complex questions, comparing providers, and forming decisions inside a single interface. That behaviour is established. The next logical step is enabling those decisions to convert directly within the interface.

LLM product queries have already been determined as higher intent than conventional search. Soon the journey will compress from intent to purchase in a single thread.

That shift matters, even in its early stages, because by the time it feels “ready,” the rules will already be set. The same pattern played out with search, marketplaces, and social platforms. Early movers shaped visibility. Late adopters optimized within constraints they did not define.

Right now, most companies are still treating ChatGPT as a research tool. Something upstream of the real commercial moment. That framing underestimates what is happening. Once a system both recommends and enables action, it becomes a distribution channel.

There will be no switch from “not a channel” to “now a channel.” There will be a gradual increase in capability, followed by a rapid change in behaviour. By then, preparation will look like hindsight.

So the question now is less “should we sell through ChatGPT today?” For most, the answer is not yet.

The better question is: what would it take to be ready?

That means thinking about how your services are structured, how your offering is described, and how a model would interpret and recommend you. It means considering integrations, data exposure, and how you participate in a system where you do not control the interface.

Most businesses have not started that work…But they will need to.

Because ChatGPT is on a path to becoming a meaningful sales channel. And like every channel before it, the advantage will go to those who prepare before it is fully formed.

The window is open now.

For the better part of two decades, technology told a simple story: intermediaries were dying. The internet would connect businesses and consumers directly, remove friction, and flatten entire value chains.

And for a while, it did.

E-commerce enabled brands to go direct. Search engines gave consumers immediate access to information. Marketplaces streamlined distribution. Layers of brokers, agents, and gatekeepers began to disappear. Control shifted toward those who could capture attention and convert demand.

Disintermediation became the default assumption.

But that era is ending.

Large language models are a structural shift in how decisions get made. Search presented options, while LLMs synthesizes them. Platforms enabled discovery, while LLMs increasingly determine outcomes.

They aggregate information, interpret intent, and recommend answers.

And in doing so, they reinsert themselves directly into the transaction. This is a concept that I have dubbed “reintermediation”.

Disintermediation saw the removal of intermediaries (middlemen like wholesalers or agents) from a supply chain, allowing producers to sell directly to consumers, often via the internet/google.

Reintermediation is the reintroduction of new, digital intermediaries like LLMs to aggregate, curate, or simplify the buying process in a crowded market.

AI systems are becoming the new layer between supply and demand. Consumers no longer browse ten websites. They ask ChatGPT. Procurement teams do not evaluate dozens of vendors. They receive a shortlist. Increasingly, the journey collapses into a single interaction, mediated by an artificial intelligence that curates, filters, and decides.

The interface actively curates, ranks, and shapes outcomes.

It shows options and influences which ones matter.

That shift carries real consequences. Power moves away from those who own the product or the customer relationship and toward those who influence the model. Visibility is no longer just about ranking highly in search results or building brand awareness. Visibility depends on being selected, summarized, and recommended by systems that operate one layer above the open web.

In a disintermediated world, success depended on access. In a reintermediated one, it depends on inclusion.

This has immediate implications for both B2C and B2B models. Direct-to-consumer becomes mediated when discovery sits inside an AI layer. Brand equity functions as a signal within a model’s decision process. Even pricing power can shift as AI systems standardize comparisons and compress differentiation.

The battleground is moving.

It centers on reaching the systems that shape customer decisions.

That raises a harder question. If LLMs are the new intermediaries, who controls them? And who gets surfaced within them?

We are already seeing the early contours of this shift. Companies are beginning to think about AI optimization in the same way they once thought about SEO. The focus shifts to appearing in answers and being chosen within them.

The irony is hard to miss.

We spent twenty years trying to remove the middlemen. Now we are replacing them with something far more powerful and influential. I suppose we didn’t want to do our own research after all…

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When it comes to discovering information, Google has dominated the last 20 plus years.

Only with the 2023 launch of ChatGPT did the search monopoly begin to crack.

Now it seems to be such common knowledge even judges in Delaware acknowledge that search is no longer a monopoly—thwarting the breakup sought by the DOJ.

Of course, some folks are saying Google is going to win again.

From far behind in 2023, Gemini chatbot is now a leader, and Google search now often serves up AI Overviews instead of blue links. Plus just announced AI shopping tools.

Still, given Google’s cannibalizing its own core search business to stay relevant, it suggests it has got some serious competition this time around.

So what is going to happen?

Well, we may be done with the Winner Take All world.

Wouldn’t be such a bad thing.

Instead, we will have multiple sources for information and analysis, each with strengths and weaknesses. Instead of defaulting to Google.

Sure, it’s true that some of the LLMs, like ChatGPT, are far too reliant on single sources. Such as Wikipedia. But even that’s changing, driven by the LLM competition. Elon Musk’s Grok recently launched Grokipedia. Pretty rough now, but it will get better.

Plus we’ve got a full plate of LLMs with real user bases. Perplexity for research, Claude for coding and no doubt more custom LLMs coming soon to disrupt every disruptible field in the US and the world. Which is just about everything in professional services, the arts and information technology.

What does this mean for your brand’s or business’ efforts to optimize for AI discovery, reputation and ultimately brand awareness and sales?

First, it means that silos are over. Google’s total dominance of search and keywords is over. Meta’s dominance of digital product advertising is next. As the Chatbots play a bigger and bigger role in recommendations and discovery.

Second, it means you need to coordinate much more effectively across all your channels to succeed in a world where no channel dominates.

Turn into our final law to learn how to make it happen.

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We’ve all heard lots of things about the consumer journey, and why it’s important. But what is it?

Well, the idea was born as far back as 1898 by someone named Elias St. Elmo Lewis. They don’t make names like that anymore. An advertising pioneer, Lewis wanted to bring scientific rigor to selling products, and in those early days of industrializing, the challenge wasn’t building a brand or relentless competition. It was consumer ignorance.

New products were flooding the market, and the public needed to be educated before they could be persuaded.

Lewis developed a model to guide a prospect to a sale. The consumer journey, in other words. You’re probably familiar with Lewis’ model: AIDA (Attention, Interest, Desire, Action). As fit the media market of those days, it was linear.

• Attention: The first crucial step. If you fail here, Lewis argued, nothing else matters.

• Interest: Transform fleeting awareness into deeper curiosity about the product’s features, utility, etc.

• Desire: Go beyond the rational mind (”I get it”) and tap the power of emotions (”I want it”). It’s where brand building starts.

• Action: If all the other stages are done right, Lewis felt consumer action was a natural, inevitable consequence.

Today, more than a century later, the AIDA model is still in marketing textbooks. It aligns with behavioral psychology. It’s core.

Until today.

The chatbots blew it up.

Perplexity, Claude, Gemini, they don’t take the consumer journey. They’re not consumers. They’re predictive algorithms. They follow the beat of their own drummer.

They travel on the Bot’s Journey.

It’s not about Attention or Interest. It’s certainly not about desire. And the chatbots can’t actually do anything but, well, what they do. Analyze language and find patterns in response to queries.

So to map the bot’s journey, we need to know where they look for information and in what sequences. In other words, what happens after the first question is asked. So here’s a shot at the new AIDA.

Relevance–the bots crawl the web looking for pertinent information based on the analysis of word patterns.

Authority–the bots look for sites with authority based on links and traffic (we think). But it doesn’t work like Google sera

They select the best sources available. That doesn’t mean the New York Times. Behind a paywall. But too often it does mean Wikipedia.

Discovery–They prefer HTML to PDFs. All technical choices must be optimized to ensure the answer engines find the right material.

Depth–They favor information that responds as clearly as possible to the initial query and follow ups.

So, if you want to make sure you’re part of the Bot’s Journey.

It’s time to get RADD!

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For decades, the communications and marketing world has operated under a single, unchallenged theology: The “Mount Olympus” of credibility. If you landed a feature in the New York Times, the Financial Times, or the Wall Street Journal, you had reached the pinnacle. You weren’t just getting a story; you were buying a permanent seat at the top of Google’s PageRank system. Search was and is snobby. It favors the professional creative classes and the legacy media giants, leaving smaller players and even major corporate brands to fight for the scraps.

But the ground just shifted beneath our feet. Per a Britopian analysis, earned media accounts for a low of 5.7% to high of 15.4% of media citations in responses to queries related to the broad technology trends including cloud infrastructure, AI/data analytics, security/compliance and collaboration productivity.

Now drill down further. Within AI answers, the competition for “top-tier” visibility is intense. Per a Gemini analysis drawing on disparate sources, AI platforms often concentrate 34% of their journalistic citations on just one or two dominant brands.

Bottom line: we are spending millions of dollars and thousands of man-hours chasing a sliver of the digital pie that represents less than 16% of how the new world discovers information.

Summary of Citation Metrics for the

“Big Three”

Why is this happening? Because AI is hungry. Unlike the snot-nosed search engines of the past that cared about the prestige of a backlink, LLMs like ChatGPT, Perplexity, and Claude are optimized to provide specific, detailed answers to increasingly granular questions. Journalism, by its very nature and business model, cannot provide this level of deep, technical knowledge. A journalist needs a “hook” and a “narrative” to drive clicks; a robot just needs the facts to fulfill a query.

This is the reality of the “Zero Click” world. When 60% of searches end without a click because the answer is served directly by the AI, the “Blue Link” economy is effectively over. If you are still measuring success by your PR firm’s ability to get you into the Associated Press, you are measuring a ghost. The real battle for influence is happening in the other 97.4%—the technical white papers, the deep-dive blogs, and the structured data that the hungry machines are actually eating.

It is time to pivot from “prestige” to “presence.” You don’t need a journalist to validate your existence anymore; you need to feed the bot.

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Gemini sources for Citation Metrics for the “Big Three”

• https://arxiv.org/html/2507.05301v1

• https://ppc.land/chatgpt-referrals-surge-as-google-searches-hit-zero-click-records/

• https://www.position.digital/blog/ai-seo-statistics/

• https://ahrefs.com/blog/ai-seo-statistics/

After nearly 17 years working with Google Ads and over a decade working with Meta ads, the most important lesson I’ve learned is that the best marketers don’t obsess over tools, dashboards, or the latest growth hacks.

They focus on the bread-and-butter fundamentals. They focus on the handful of metrics that actually move the needle. And most importantly, they understand their customers deeply.

Recently, I’ve been hearing a message more and more, especially from people inside the ad platforms themselves:

“You don’t need to worry about audiences anymore. Just upload great creative and let Meta find the customers.”

On paper, it sounds great. Bring good creative, a good offer, and a solid landing page, and Meta’s algorithm will figure everything else out.

But after years of running campaigns and watching brands scale (and fail), I can tell you there are three big reasons why that approach doesn’t actually work in the real world.

1. Meta Optimizes for Meta — Not for You

The first thing every advertiser needs to remember is simple: Meta’s algorithm is optimized for Meta’s business first.

Yes, you can select campaign goals like: Conversions, Leads, Purchases, Email signups

But at the end of the day, the platform’s primary incentive is to maximize ad spend and platform efficiency, not to guarantee that your brand wins.

If every advertiser is handing the algorithm full control, then the system ultimately decides which advertiser gets the best audience.

And guess what? Meta has no loyalty to your brand. If you’re going head to head with a large incumbent brand that is spending 10x your budget, guess who META gives the algo love? Not you!

2. No One Will Ever Know Your Customers Better Than You

One of the biggest common traits I see among brands that scale successfully—especially in direct-to-consumer—is that they deeply understand their customers.

Not just basic demographic targeting. I’m talking about real customer intelligence.

The brands that grow fastest are the ones that:

• Download and analyze their customer data

• Build detailed customer personas

• Study spending behavior across thousands of customers

• Segment audiences based on real purchasing patterns

Instead of thinking about a customer as: “Jane Smith in Mississippi.”

They understand something closer to: “Jane Smith, age 35, two kids, spends $120 every three months, typically purchases after seeing educational content.”

The platforms claim their algorithms build these personas automatically. But in practice, the brands doing their own analysis almost always outperform the ones that rely purely on automation.

Industry data supports this as well.

• According to Meta’s own marketing science reports, campaigns using first-party data and customer segmentation can improve return on ad spend by up to 2–3× compared to broad automated targeting alone.

• Research from eMarketer and Nielsen has shown that brands leveraging first-party customer insights improve conversion rates by roughly 20–30%.

• Meanwhile, Gartner reports that companies using customer data for personalization generate up to 40% more revenue from those activities than average competitors.

The takeaway is simple: Algorithms are powerful. But they don’t replace customer understanding.

Every practitioner I’ve seen who says: “It’s all creative now. Just let the algorithm handle everything”...ventually runs into problems. Because creativity without customer insight is just guessing.

3. The Algorithms Change Too Fast

The third issue is something that’s gotten significantly worse over the last few years:

The pace of change inside ad platforms has accelerated dramatically. Both Meta and Google update their algorithms constantly.

A campaign will perform well for a while. Then suddenly performance collapses. The feedback sounds like this:

“Everything was working great… and then two weeks ago it just died.”

When advertisers rely entirely on automation, they often lose visibility into what actually caused the change. The speed of algorithm changes means that blind trust in automation creates instability. The more control you give up, the harder it becomes to diagnose problems when things break.

The Hidden Cost of “Learning Mode”

There’s another detail most marketers overlook. Every algorithmic ad platform—including Meta and Google—relies on something called learning mode.

Learning mode essentially means: “Let us spend your money while we figure out who your customers are.”

Think about that for a moment. The platform is using your budget to discover information you should already know. You’re paying for the algorithm to learn. But if you truly understand your customers, you can start much closer to the answer from day one. Which means less wasted spending.

At the end of the day, the brands that succeed in digital advertising aren’t the ones who rely entirely on automation. They’re the ones who do the hard work most companies avoid, which is knowing their customers inside and out. Then they use platforms like Meta and Google as distribution engines, not decision makers.

Bottom line: brands that aren’t investing in customer intelligence today are probably leaving 15–20% of potential revenue on the table. In modern digital marketing, that’s a gap your competitors will happily take.

Your website, your most likely boring corporate website, is where you need to start.

I can already hear the objections.

I don’t want to mess with the site.

Do we really need to publish more content?

Nobody reads it anyway…

Stop. Right. There.

You’re fighting the last war–the site visits, user engagement and SEO battles.

We’re in the age of AI. Don’t forget law number one, the most important law.

AI is now your most important audience. And per law four, you need to write for the robots, not just people.

There’s no better, no more efficient place to do that than on your owned digital properties–your website at the core.

So what do you do exactly?

You use law six, know thy queries, to map out the 40 to 60 most important questions for your brand. You identify where you appear, what’s right, what’s wrong and what’s missing.

Then you create articles, charts and videos for the robots.

You don’t worry about narratives for humans who expect to be pulled into an article or story or video.

You answer the questions clearly and specifically.

Every single one of them.

You add pages to your website–not for the human journey but for the bot’s journey.

You make sure the pages are discoverable.

You structure your sentences around the positive sentiment terms associated with your business, brand and product.

You isolate the negatives.

Why because machines don’t read the way we do. Law four!

Then once you see the pages index in google–the LLMs still use google to find pages–you rerun your key questions.

You track the changes.

If you are thorough and publish quality, detailed answers, you will be surprised how quickly you can impact results.

It’s the golden age of AIO, as we’ve said before.

Your website is your most valuable resource.

Use it.

In the next video, we take you a step further to track just where the bots are going. So you get the right information to the LLMs in the right places.

Over the weekend I spent a lot of time digging into the hype around OpenClaw. If you spend even ten minutes on social media right now, you’ll see posts claiming things like “I built a seven-figure business in 30 minutes with OpenClaw.”

If you scratch beneath the surface, most of those posts are just clickbait. But after spending the weekend actually trying to configure and run OpenClaw myself, I came to a different conclusion:

OpenClaw is already dead. But here’s the twist: while it’s a zombie project walking toward irrelevance, it has accidentally opened the door to an entirely new industry.

The Idea Behind OpenClaw Was Brilliant

To understand why OpenClaw matters, you need to understand where it came from.

A developer originally created a project called Claudebot, which later became Open Claude (to avoid trademark issues) and eventually evolved into OpenClaw. The core idea was simple but powerful: Instead of sending all your data to the cloud, run AI locally on your own machine.

In practice, that meant something like this:

• Take a small computer like a Mac Mini

• Turn it into a personal AI server

• Connect it to your preferred large language model

• Let it manage tasks across your system: Access your calendar, Automate workflows, Process media files, Create content and distribute it across platforms

Naturally, I thought: I need to try this.

Then the Problems Started

Actually implementing OpenClaw revealed the real story. First, the governance structure is messy. The original developer eventually joined OpenAI, and while OpenAI has shown support for the project, OpenClaw itself is maintained by a decentralized community of engineers.

That’s great for experimentation. But it’s terrible for enterprise reliability.Then there were the technical headaches.

Unless you configure everything perfectly, OpenClaw breaks constantly. Installing dependencies like Node.js can fail depending on the environment, hardware configurations vary wildly, and the interface is—being generous—rough. Even experienced engineers can run into issues.In other words, OpenClaw feels exactly like what it is: A community-driven open-source experiment. Not an enterprise platform.

But OpenClaw Proved Something Huge

Despite the problems, OpenClaw revealed something that might be even more important than the product itself. There is massive pent-up demand for private AI infrastructure.

People want AI tools that: Run locally, Protect sensitive data, Avoid massive cloud compute bills, Operate directly on personal or enterprise hardware The numbers back this up.

The global private AI market is projected to grow from $11.1 billion in 2025 to $113.7 billion by 2034, expanding at nearly 30% annually. (Dimension Market Research)

Meanwhile, the AI server market is expected to explode from roughly $124 billion in 2024 to more than $850 billion by 2030. (Grand View Research)

The Industry Has Already Noticed

The moment something proves a massive market exists, the big players move in.

• Perplexity just launched a new system called Personal Computer, designed to turn a Mac into a 24/7 AI agent running locally on your machine. (The Verge)

• Hardware companies are shipping devices that run local AI models out of the box, including AI-ready NAS systems with OpenClaw preinstalled for tasks like video editing and automation. (Tom’s Hardware)

• Lenovo has introduced powerful desktop AI workstations designed to bring large-model development directly onto personal machines. (Lifewire)

• At the same time, tech giants are investing billions in specialized AI chips and infrastructure to support the next generation of AI computing. (Reuters)

What the Next 365 Days Will Look Like

OpenClaw showed the market. Now the giants will build the real products. In the next year, expect:

• Hardware vendors shipping AI-ready machines

• LLM providers releasing local AI systems

• Enterprise platforms integrating private AI servers

Imagine a future where Microsoft Copilot, or Gemini offers two modes:

Cloud Mode: Heavy processing, Shared enterprise data, Massive compute workloads

Local Mode: Sensitive documents, Video processing, Internal company workflows, Private datasets

Instead of uploading terabytes of data to the cloud and paying for compute every time, organizations will run AI directly on local infrastructure. Consumers want this. Enterprises need this. Industries like healthcare, law, finance, and consulting practically demand it.

Why OpenClaw Won’t Survive

The irony is that OpenClaw started this conversation but probably won’t win the market it created. Why? Because it has three structural disadvantages:

1. Complex setup

2. Weak user experience

3. Decentralized development

Meanwhile, the companies entering this space have: billions in capital, dedicated engineering teams, hardware integration, massive ecosystems Once companies like Microsoft, Google, Nvidia, or Perplexity release polished alternatives, OpenClaw will struggle to keep up. Not because it was a bad idea. But because it was the prototype.

The Real Legacy of OpenClaw

History is full of technologies that changed everything but never dominated their own market. Think of: Netscape in the browser wars, Friendster in social media, Palm in smartphones

They proved something was possible. Then the giants arrived. OpenClaw may end up being the same. A year from now, no one may be talking about OpenClaw itself.

But everyone will be talking about something it helped reveal: The future of AI isn’t just in the cloud. It’s on your desk.

Welcome back to SEO is Dead’s Ten Law of AI Optimization. Let’s quickly recap where we’ve been.

Law one: AI is your brand’s most important audience.

Law two: Search engines are snobby, but AI is hungry for information, deep information. It’s the greatest opportunity for brands and businesses since the launch of the internet.

Law three: to win, you need to understand how machines read.

Law four: you need to write for robots too. If you want to make it into the Chatbot answers that matter.

Law six: algorithms are moving target, and you must act now to seize the golden moment for AIO.

Now let’s get to law six. You must know yourself to know your queries.

Sounds like pretentious nonsense, doesn’t it? Against the spirit of our laws.

But here’s the rub. To draft the right answers, you need to know the right questions, and the machines won’t tell you. They are black boxes. Google gives you a lot more. Such as the volume of searches for a keyword. ChatGPT doesn’t tell you which questions are asked most frequently.

You have to take a different approach.

If you start with a clear sense of who you are or want to be–easier said than done sometimes–but there’s no way around it to optimize for AI discovery.

Here’s the process:

First, you come up with a query list. At least 40 to 60 questions.

Second, you divide the queries into branded (using your name) and non-branded (broader category questions).

Third, you ask the questions in at least three LLMs. We like Chat, Perplexity and Google AI Overviews.

Fourth, you use the best tools available to support you, identifying where you appear and which sources the LLMs cite.

Fifth, you dig in and analyze the results.

You will learn what ChatGPT knows about you and your category, and what it doesn’t know and cannot find. You’ll discover a competitor performs better in Perplexity, and you’ll be surprised to find that Google AI Overviews rely more on your website and ChatGPT favors Wikipedia.

You’ll discover which answers you shape, which answers you don’t, and where the opportunities and challenges are in educating the Chatbots and, ultimately, your consumers or customers.

In the next law, we’ll explain why this approach to Artificial Intelligence Optimization–AIO–is urgent.

If you miss the window to shape the questions, you may not get another.

How AI Is Coming for Service Industries You’d Never Expect

I was watching a ski video with my kids the other night. They’re at that age where skiing is still half excitement, half terror. So I like them to see proper technique before we hit the mountain.

The instructor in the video was from the French Alps. Calm. Technical. Clearly world-class. And at one point he stopped and said something that caught me off guard:

“Please support real ski instructors. Don’t rely on AI-generated videos to learn how to ski.”

I said what! AI-generated ski videos? So I searched.

And sure enough — YouTube is now full of AI-stitched ski tutorials. Generic voiceovers. Stock mountain clips. Sloppy animations explaining turns and braking. It’s what we’d call AI slop. Low effort. Mass-produced. The sport itself has never been more popular. So we can see why people who don’t know anything about skiing would try to monetize via AI videos.

Skiing Is Growing. So Is the Pressure.

Since the 2022 Winter Olympics, winter sports participation has seen a measurable bump. The National Ski Areas Association reported one of the highest skier-visit seasons on record shortly after the Games — more than 65 million visits in the U.S.

Demand is strong. At the same time, costs are the highest they’ve ever been.

Lift tickets at major operators like Vail Resorts and Alterra Mountain Company can exceed $200 to $250 per day at peak. A private two-hour lesson can easily range from $400 to $800 in many markets. Group lessons often approach $200, and that’s before you factor in rentals, lodging, food, and travel.

So what happens? People look for substitutes. They show up on the mountain, skis on for the first time, phone out, watching a video about how to wedge stop.

I spoke to multiple instructors in the Northeast this season. They all said the same thing: skiing is busy and they’re working, but more people are trying YouTube first.

That’s the pattern. Demand is up. Costs are up. AI instruction is up. That’s not unique to skiing. That’s the blueprint for how AI enters service industries.

Now Fast Forward Five Years

Imagine this.

You arrive at the mountain. Instead of booking a human instructor, you rent a humanoid robot powered by something like Optimus. It skis with you. It stabilizes you through turns. It picks you up when you fall. It analyzes your posture in real time and adjusts your movements with mechanical precision no human coach could match.

Or imagine you’re wearing Meta-powered AR glasses from Meta Platforms. As you approach a slope, the glasses tell you the grade, the snow condition, and the recommended turn radius for someone at your ability level. They alert you to brake in fifteen feet. You see a holographic instructor in front of you demonstrating the move before you execute it.

You record your run. The system auto-generates a breakdown explaining that you leaned back seven percent too far in your last three turns.

That world is not science fiction. It’s inevitable. The hardware will catch up. When it does, what happens to ski instructors?

The Barbell Effect

AI does not eliminate industries. It eliminates the middle.

The mediocre instructor who simply repeats standard drills without personalization becomes replaceable. The elite instructor who embraces AI becomes harder to compete with than ever.

This is the pattern I’m seeing across service industries. If you’re average and analog, AI compresses your value. If you’re exceptional and augmented, AI multiplies it.

How a Ski Instructor Becomes 10x More Valuable

If I were a top ski instructor today, I wouldn’t resist AI. I’d build on top of it.

First, I would create real instructional content — not AI slop, but high-quality lessons where I am the expert on camera. I would use AI to enhance production with overlays, angle diagrams, slow-motion analysis, and mountain mapping, but the authority would remain human. As low-quality tutorials flood the internet, credibility becomes more valuable, not less.

Second, I would implement intelligent pre-lesson diagnostics. Before a student even arrives, they would complete a structured intake that captures athletic background, prior skiing experience, fear level, injury history, and specific goals. That data would feed into a system that helps design a customized lesson plan in advance. Instead of guessing on the lift, I’d be diagnosing from the first turn.

Third, and most powerful, I would use AR recording tools to generate post-session performance reports. Imagine recording thirty minutes of instruction and then instantly producing a personalized breakdown with annotated clips, specific corrections, and drills to practice before the next session. The lesson would no longer end when the skis come off. It would continue at home.

That doesn’t replace instruction. It upgrades it.

Will Robots Replace Ski Instructors?

Eventually, robots will exist on slopes. Eventually, AR overlays will teach beginners the basics. Entry-level instruction will likely become partially automated.

But technology analyzes mechanics. It does not read fear in a ten-year-old’s eyes at the top of a black diamond. It does not calibrate encouragement in real time. It does not understand when a student needs confidence more than correction.

Confidence, judgment, and emotional intelligence and deep expertise in coaching humans are not easily automated.

When you combine human emotional intelligence with AI performance analytics, you don’t eliminate the instructor. You evolve the role into something stronger.

The Bigger Pattern

This isn’t about skiing. It’s about every service industry that assumes it’s insulated.

Personal trainers, tennis coaches, golf pros, physical therapists, and many consultants are all on a similar trajectory. AI first enters through lower-cost digital content. Consumers experiment. Hardware improves. Performance analytics mature. The lower tier gets commoditized. The top tier gets amplified.

The Future Belongs to the Augmented Ski Expert

If you are great at what you do and you embrace AI, your value increases. If you ignore it, your value compresses. Five years from now, you might see a robot skiing.

But the human who understands how to combine craft, judgment, and intelligence — that’s the one people will still book, and likely pay more for. Same slopes. Same snow. Different economics.

SEO & AIO: The Rise of “Zero-Click” Primacy

New industry benchmarks released yesterday reveal that Google AI Overviews (AIO) now appear in over 25% of all searches, with Health Care reaching a staggering 49%. The report confirms that traditional SEO is no longer about the click, but about “Brand Visibility” within the summary.

• Key Stat: AI referral traffic is growing 1% month-over-month, with ChatGPT driving nearly 87% of all AI-based referrals to websites.

GEO: “Earning the Citation” replaces “Gaming the Link”

The shift toward Generative Engine Optimization (GEO) has hit a tipping point. New strategies emphasize “Modular Content Design”—writing in self-contained, high-density paragraphs that AI can easily “clip” and cite.

• The Strategy: For B2B and tech brands, GEO is now driven by earned media. AI engines are prioritizing journalistic sources (25% of citations) and recent content (published within the last 7 days) to ground their answers.

AEO & GEO: Multi-Modal Optimization

A major 2026 guide on Answer Engine Optimization (AEO) highlights that AI assistants are no longer just reading text; they are “viewing” content. Optimization now requires Visual AEO, where images, feature comparison tables, and instructional diagrams are structured specifically for AI “vision” to extract and present as the definitive answer.

• Trend: The “Answer-First” format (placing a concise 40-60 word answer at the very top of a page) is now the required standard for capturing the “Position Zero” voice response.

  

The News: Google officially made Gemini 3 the global default for AI Overviews and introduced Gemini 3.1 Flash-Lite. They also rolled out “AI Mode,” allowing users to jump directly from a search summary into a continuous back-and-forth conversation.

• AIO Impact: AI Overviews (AIO) are now more conversational. The “rank + click” model is being replaced by “cite + converse,” where being the source of a follow-up answer is as valuable as the initial summary.

2. Apple Unleashes M5 Chips with “Neural Accelerators”

The News: Apple announced the MacBook Pro with M5 Pro and M5 Max chips, featuring a dedicated Neural Accelerator in every GPU core.

• GEO/AIO Relevance: This hardware push signals a shift toward Local GEO. As more AI processing happens on-device rather than in the cloud, brands will need to optimize for “Edge AI” models that prioritize local data and privacy-compliant content structures.

3. Rise of “Agentic Commerce” & LLM-first Content

The News: 25% of content marketers now identifying LLMs as their primary audience, the focus has shifted to “Agentic SEO.”

• AEO/GEO Strategy: Tactics are moving away from keywords toward Answer Blocks and llms.txt files—a new (though experimental) standard used to tell AI agents exactly how to parse and recommend your products during a “buying agent” session.

Watch Video

When search engine optimization first got going, it wasn’t hard if you put the work in.

If you published high quality content, Google recognized it. Like we did for GE, Credit Suisse, Dell, Pfizer and many others.

Some of our work is still sticking to the side of search, like a barnacle on a ship.

Try a search for How Loud is a Wind Turbine. The infographic you see. We created it for GE more than a decade ago.

Back then, media outlets were hungry for content they could monetize via ads. Business Insider even syndicated our investment insights from Credit Suisse.

It was the golden age of SEO.

But like all good things, it came to an end, and it became much harder to rank in search queries. Both because Google changed its algorithm again and again and because new media became more important. Namely, video and social media.

Suddenly, you needed paid support to get noticed. Very convenient for Google and Meta.

We’ve been stuck in this increasingly inefficient and expensive loop until ChatGPT burst onto the scene.

Information discovery changed, and we’re now in the golden moment for AIO.

If you follow the laws of AIO, if you know your objective, purpose, mission, if you know and analyze your queries, you have the opportunity of a lifetime to influence the AI answers that matter to you.

But this window will close.

Not tomorrow. Probably not in 2026. But the algorithms behind ChatGPT, Perplexity, Google AI Overviews, they will change.

They will have to.

Right now, even subscribers, paying customers, are being subsidized.

A GPT pro account costs $200/month and…Sam Altman confessed to TechCrunch it’s still a money loser.

The LLMs will have to make money, and when they do, they are going to integrate advertising, and they are going to stop ingesting content at the same pace and in the same way.

When that time comes, if you’re not ALREADY in the right answers in the right way, it may get as hard to break into the chatbot responses…as it is to land the first page in Google search.

Now is the time to accept the information discovery has changed and move fast.

In the next law, we tell you how to get started.