From Newtonian to Quantum: Why brand authority in the AI era is not your traditional SEO

In my last post I shared my experience using Perplexity to research a purchase. Every time we use an Everything Machine (e.g. Perplexity, ChatGPT, Gemini, etc) to research products, it comes at the expense of a “traditional” Google search. Gartner predicts search engine traffic could drop by as much as 50% by 2028. There is evidence that we’ve hit peak SEO and the Everything Machines are on the rise.

When I was running Influencer Marketing Hub, I saw this change first hand. Our traffic was fluctuating wildly due to Google’s increasingly frequent core updates and the introduction of AI Overviews. At the same time, we started to notice incoming traffic from ChatGPT and Perplexity. While SEO traffic is declining and Everything Machines traffic is increasing, the two can’t be directly compared. The nature of the technologies, user experience and traffic make them fundamentally different. 

Traditional SEO: The Newtonian Physics of Go-To-Market 

Traditional SEO operates much like Newtonian physics—a deterministic system with clear cause-and-effect relationships:

• Predictable Outcomes: Specific keyword strategies and backlink profiles predictably affect brand rankings in search results

• Measurable Metrics: Rankings, CTR, conversions, and lead generation are precisely  measurable, trackable, and relatively consistent

• Rule-Based Environment: Google's algorithms, while complex, follow discernible patterns that brands can observe, document, and leverage

• Linear Improvements: Incremental changes (optimizing product pages, building industry backlinks) typically yield proportional results in traffic and conversions

Brands succeed by optimizing around these "laws" of search engines. They conduct competitive keyword research, optimize product pages, build quality backlinks, and refine conversion funnels—all with relatively predictable outcomes for customer acquisition.

Everything Machine AI Optimization: Brand Authority in the Quantum Realm

Establishing brand authority for the Everything Machines and their large language models enters a realm similar to quantum physics -  probability replaces certainty, and multiple states exist simultaneously:

• Probabilistic Authority: Like quantum particles that exist in multiple states until observed, a brand's representation in AI outputs varies based on the totality of its digital footprint and contextual factors. Multiple LLM prompts on the same topic result in differing results. 

• Emergent Brand Presence: Minor changes in how information about your brand is structured online can trigger completely different AI representation patterns

• Contextual Variability: The Everything Machines produce different outcomes with seemingly identical inputs. Your brand’s information can be interpreted differently based on who's asking about your brand, what they are asking and how they phrase their question, much like quantum superposition where particles exist in multiple states simultaneously. 

• Share of Mind vs. Share of Search: Success comes from understanding how to become the probabilistic "best answer" rather than the top-ranked link

In this quantum-like environment, brands are competing for share of voice and authoritative representation rather than simply ranking position or click-through rates.

Why This Distinction Matters

This fundamental difference demands a shift in how brands approach digital visibility:

SEO Thinking: "If we target keywords X with content Y and build Z backlinks, we should rank on page 1 and drive conversions."

AI Optimization Thinking: "If we establish ourselves as the authoritative source on topics related to our products and services across multiple dimensions, we increase the probability of being cited and recommended when users engage with AI systems."

Where traditional digital marketers might focus on conversion optimization and keyword targeting, effective AI brand relevance requires:

1. Authority Building: Becoming the definitive source on topics related to your products and services

2. Comprehensive Information Architecture: Structuring information about your brand, products, and expertise in ways AI systems can confidently understand 

3. Multi-dimensional Presence: Publishing diverse, high-quality content and data that reinforces your expertise from multiple angles and sources

4. Training Beyond Guidelines: Inserting informative brand data in unexpected and appropriate places to support imitation learning 

5. Consistency Across Ecosystems: Maintaining consistent brand messaging and expertise across the entire digital landscape

Bridging Both Worlds

Tomorrow’s successful brand will increasingly require mastery of both approaches. The deterministic world of SEO still governs direct customer acquisition, while the probabilistic nature of AI systems determines how your brand is represented when customers ask questions about your industry, products, or solutions.

As we navigate this transition, brands that can operate in both "Newtonian" and "Quantum" terms will have the lasting advantage—understanding when to optimize for direct traffic and conversions, and when to build multi-dimensional authority that makes you the probabilistic "best answer" when users engage with Everything Machines. 

Does this comparison resonate with what you are seeing? Leave a comment and let me know. If you are a Brand and want to discuss how to adapt your go-to-market approach for AI-mediated authority, I’d love to connect.

The future of buying

A couple of weeks ago, I started daydreaming about upgrading my hi-fi setup. I asked Perplexity to find reviews of my speakers paired with a specific integrated amplifier. What happened next perfectly captures how we will make buying decisions in the future.

Perplexity informed me that there were no reviews of my speakers paired with the specific amplifier I was asking about. However, it did read the reviews of my speakers and noted their power requirements. It then compared those requirements with the power output of the amplifier I was considering. It recommended alternative amplifiers that would be better suited to my speakers' power requirements.

Perplexity became my personalized audiophile product specialist, helping me synthesize information from multiple sources, performing analysis, and not only answering my specific question but also going one step further and making alternative recommendations. It is just a matter of time before it asks me if I want it to find the best prices on both new and used models of the recommended amplifiers and order one for me.

The powerful thing here is how Perplexity focused on directly answering my question. This is a far superior customer experience compared to having to wade through marketing brochures and technical specifications myself. In fact, I’m starting to trust Perplexity more than any individual review site or brand marketing site.

Admittedly, my example is a high-consideration, high-ticket purchase that benefits from technical knowledge and analysis. However, there are many purchases like this over the course of one's life. And once you get used to having a personal product specialist for these purchases, why wouldn't you expect one for every non-routine purchasing decision?

Google’s semi-secret agent advantage

The AI Internet is already all around us in the form of the current Everything Machines. Their owners are, very obviously, unsatisfied with their current forms and are continuing to push their development rapidly and very expensively forward. This evolution is from Answering Machines to Acting Machines, more commonly referred to as agents.

Next Best Word

Building Answering Machines, even these imperfect ones, has been an incredible task, but a pretty easy one to summarize. At their core, all these massive GenAI systems do is predict the next word that a human would type in the context of whatever summary, essay, report, or story is being asked of them. We are predictable enough as a species that, given all the text on the Internet and a multibillion dollar cloud infrastructure, most new poetry and prose can be created for us if we request it. The same goes for images and videos. The next pixel or frame can be extrapolated with increasingly high fidelity just as the next word can. We finally have our infinite monkeys on infinite typewriters.

This also means that they are implicitly judging our text against the statistical norms used across the internet. Do they like what we wrote and lift it? Or, do they not and rewrite it? Or, would they prefer to be fed structured information in the form most economic for them and not worry about our prose? (It’s that last thing in case you are wondering.)

Next Best Action

The same method and math applies to agents. Agents will have to predict the next human action to reach a similar level of consumer satisfaction as the Everything Machines enjoy today. They will happily consume your site map and some Q&A you target at them, but that’s not how their statistical models are built. Their models will be built in the same way as GenAI above: gather enough human clicks on all the world’s websites – to predict the next click. This is a task which is effectively impossible with the datasets available. Our clicks – and the order of those clicks – have not been recorded as our words, images, and videos have been. Clicktracking is currently the stuff of spies and stalkers; but we’re going to have to think of those people as early adopters. In the legitimate world, Google Navboost is by far the most comprehensive in this regard and creates a significant advantage for Google Gemini’s agents.

Google’s Advantage

Between their unique Google Analytics and Chrome data, Google Navboost uses millions of SEO “partners” to build a record or what we all click on, creating a statistical map of how humans use online properties. Those statistics are exactly the model Google needs to roll out its initial agents, which will by then be trained by us to Act just as the current Gemini has been trained by us to Answer. To drive conversions out of Gemini and its upcoming agents, we all need to participate. However, to passively hand Google the monopoly on global clicktracking is going to make them money to the exclusion of the rest of us.

Avoiding a rerun?

We all know what a Google monopoly looks like. It is in everyone else’s best interest to ensure that doesn’t happen again. The way to avoid it (and make money in the process) is to properly train Google’s Everything Machine competitors with the data ammunition they need to compete. Otherwise, we’ll find ourselves fighting (1) Yelp’s fight all over again, or (2) over the scraps Gemini leaves for us. One significant technique for arming OpenAI and later, Apple Intelligence, is to emulate the aspects of Navboost that we can execute on practically and legally. Capture our own clickstreams, de-identify them if appropriate, and feed them to the Everything Machines today. Even Apple; even with their schedule slippage. Make sure that the LLMs that deploy and control the agents can build a statistically significant map of how your users navigate your sites. Teaching three armies of action beats watching Google dominate the agenetic internet the way it did Search & Discover.

Provide offerings to the Everything Machines, and you will prosper

No matter how determined OpenAI, Google, and Apple are to fight each other for AI market share, we have options to succeed with, between, and around them. This post is about “with.” Succeeding with the Everything Machines means feeding them the data they need now to grow their market share now. They have learned everything they can from historically open web content; now they will be reliant on partners to provide information that they could not readily crawl a year ago. They will also learn who the most effective  partners are and will build the habit of relying on them for as long as that partner performs. That is the opportunity.

As with everything we do professionally during massive Internet platform shifts, this is a high risk/high return opportunity. However, there is no clear way to play it safe. Inaction leads to irrelevance and loss of market share. There are multiple ways to succeed with the Everything Machines; those paths to success all rely on what training data you can offer them this year. You should have Four Goals and you need to get started now.

Goal #1: Determine your company’s AI starting point online and off.

Method: Assess your current AI consumer efficacy versus your competitors via an audit. Get your executives focused on the kind of information that matters to learning systems.

• Build a SWOT analysis, one which includes new adjacent opportunities, to analyze how the Everything Machine’s domination of current Internet middlemen will affect enterprise revenue.

• What partner and channel programs outside of those middlemen are likely to be affected?

• Do we expect those partners to be aggressive enough in transforming their own businesses in this new AI environment?

• What conversations is my brand showing up in today based on my legacy SEO?

• What missing topics should the Company care about and which current ones are extraneous?

• Tactically, which of our owned web pages and partner content should we immediately try to shine a spotlight on for the Everything Machine’s crawlers?

Goal #2: Engage with the current generation of AI crawlers, which is actionable today. The systems that provide the crawler with their data gathering assignments will shape the initial hordes of agents that will start appearing in a few months.

Method: Hire or contract your way into understanding the data formats that the Everything Machines prefer.

• Certain data formats clearly cause the AIs to overindex on (i.e. pay more attention to) the sites that publish them. Those file formats are far less expensive for the Everything Machines to process and include as updates; they are just acting in their own self interest.

• Understanding the strategic goals of Google’s Navboost, which gathers a site’s clickstreams, is a great exercise for executive teams and one we are increasingly being asked to lead. As Google Gemini learns the statistical map of how humans navigate sites, that will become the starting point for how their Project Astra agents start navigating.

• Be aware now of Anthropic’s Model Context Protocol at a high level. It is focused on the internals of large enterprises, not their consumer-facing systems, but it shares many characteristics of the other agent APIs which will launch shortly.

Goal #3: Engage with the agents from OpenAI and Google as soon as their developer interfaces are announced, likely in Q2 2025. They will seek to look inside your systems via any APIs that you have built over the last fifteen to twenty years. All of the nerds like me who yelled about API-first enterprises are finally about to have our moment of extreme value creation. #mashery

Method: Gain an executive view into your APIs, public or private.

• Understand which ones are the highest value to the Everything Machines and make the most aggressive possible decision about sharing it. Unlike the current crawler tactics, there will be some security around the information shared here, though PII will only be shareable in the Apple Intelligence case below.

• Clean up your API documentation and evolve it into a direction easily consumable by the Everything Machines. What is obvious to use is not obvious to them, and sometimes vice versa.

• Have a talent plan; coding is getting more automated but it is nowhere near a panacea yet. As we’ve all suffered from in other Internet booms, the demand for humans who can write to the Everything Machines’ APIs will be intense. Get ahead of it.

Goal #4: For iOS app owners, be an early partner of Apple Intelligence to benefit from their need to minimize traffic to ChatGPT. Per my initial post, that means offering up your most precious data to gain market share fast. You know your best customers in your area of engagement better than anyone else.

Method: Teach Apple Intelligence about that one customer on that one smartphone so they deliver you many similar smartphones.

• The data formats advice above applies here too. Within the phone, Apple will seek data formats very similar to what the AI internet crawlers also prefer and for the same reasons.

• Unearth your most valuable data, including PII in this singular case, and encapsulate that data in these on-phone AI formats.

• The talent issue is also at least as critical here.

The emergence of Everything Machines presents an opportunity that businesses cannot afford to miss. With major players like OpenAI, Google, Apple, Meta, and Anthropic vying for dominance, the market is wide open in ways we haven’t seen since the original proliferation of search engines.

The current landscape, with its abundance of Everything Machines, is advantageous for businesses  who are willing to adapt. These tech giants need a broad scope of specialized knowledge and expertise to succeed against each other. By collaborating with them and sharing your unique data and insights, you can leverage their consumer reach and position yourself for growth.

It is essential to proactively engage with these companies and develop a strategic plan that benefits both parties. By providing them with the resources they need, we can tap into their vast user base and gain significant exposure for your business.

The next internet evolution

For most of the history of the Internet, it's been a tool built by and for humans. A lot of effort has been spent on how to make the Internet easier to use. First there was the browser, then the search engine, SaaS, smartphones and finally apps. Yes, this is a gross simplification, but a useful one to help understand where we are going next. 

The browser made navigating the internet less complicated. Search engines allowed us to find the proverbial needle-in-the-haystack and became the backbone of online marketing. SaaS made delivering sophisticated software solutions scalable to businesses of all sizes.  Smartphones made the internet portable and ubiquitous. And finally apps made it easy to address every conceivable use case from sharing silly videos to instant food gratification.

AI is changing all of this. We are evolving from the Human Internet to the AI Internet. On the Human Internet we developed sophisticated software to make it easier to use. On the AI Internet we will have machine-built agents that complete tasks on our behalf.  Today’s AI Internet takes the form of LLMs that are handy creative and research interns. But with OpenAI Operator, Google Project Astra and Apple Intelligence, tomorrow’s AI Internet will be replete with agents, mediating online activities on our behalf.  

We are already starting to see the impact of this transition in SEO. Google is aggressively cannibalizing its search business in favor of Generative Search. The impact is that Search Engine Results Pages are rapidly evolving from a jumping off point to the right parts of the Internet to the one-and-done destination for your query. Recent research found that nearly 48-percent of Google SERPs can include an AI Overview. This pushes links below the fold in favor of a summary of what's below the fold, eliminating the need to scroll down and click through to the sites listed. 

Google’s AI is interpreting your intent and serving you a synopsis that is likely good enough. It is going beyond curating the best links for you to intermediating your interaction with the Internet. When it’s right, it’s a great user experience. 

This completely changes the business model of the media businesses that used to benefit downstream from Google. Those businesses monetize that traffic through advertising, subscription sales or some combination of both. They are going to have to rethink their business models and value propositions for the AI Internet (we can help here). This is like the Napster moment for the text based web. The music business model evolved from selling physical media to streaming, live performance and merch. 

The online content and media business is probably due for a cycle of disruption to weed out sites that are more noise than signal. What happens as we turn to AIs for help with product discovery and buying decisions? This will go beyond disruption and fundamentally change the nature of buying and selling. More on this in the next post.

AI killed your SEO. That's just the start.

OpenAI, Google, and Apple take their current AI hegemony seriously enough that there is no relationship with them so valuable that they won’t sacrifice it. It’s not simply a fight over AI search or agents, it’s the effort to build the winning Everything Machines, similar in thinking to but carrying far greater significance than Amazon’s Everything Store.

OpenAI is betting that their stunning early market share lead, built as ruthlessly and leveraging as much hyperbole as tech has ever seen, and Operator agent services will make them the Everything Machine of choice. Google has already sacrificed their stunningly valuable SEM and SEO ecosystem to the effort; no individual customer within those could possibly count. Apple would prefer that their existing app providers fall into line very quickly with Apple Intelligence and thrive, but they will move on to your competitors fast if it benefits them. For those of us for whom iOS apps are the right distribution channel, the Apple opportunity is superior. Regardless, we either help one or more of these Kaiju build and grow their Everything Machines or lose our businesses to them – or both if we do not execute well.

The death of SEO, the wholesale abuse of copyright, and the further carbonization of the planet are nothing compared to the upcoming battle between flexible AI agents: OpenAI Operator, Google Gemini Astra, and (effectively) Apple Intelligence. Nerds that read this won’t like that I conflated Apple Intelligence with the two major formal agent infrastructures, but my worldview and consulting work are focused on the functional equivalencies of replacement technologies. With enough of a head start and the right prevailing winds, I’ll race my hot air balloon against your jet any day. For me, the last decade-plus was a successful portfolio bet on a “toy” ecommerce system called Shopify, which I was told repeatedly would never catch up with “real store software.”

Apple Intelligence’s “toy AI” has many of the same characteristics – OpenAI and Google need to take more margin from their third parties to perform financially. The thousand-dollar candy bar that Apple has in our pockets gives them both the opportunity and incentive to make room for more and healthier third parties. OpenAI’s and Google’s challenge is that their AI task is far more difficult. The core of their Everything Machines are much more massive – imagine spreadsheets a trillion-plus columns wide and long but related across tens of thousands of dimensions instead of the two dimensions on our screen. In terms of AI compute expense, that trillion-plus columns across are the challenge. They have to capture each structural weakness and exception in (at least) the English language, multiplied by all the other exceptions.

Contrarily, Apple already has a primitive Everything Machine in our pockets and on our wrists, which gives them a tremendous advantage here both in terms of timing and approach. Their Everything Machine is small enough to live on our phones – which we pay for upfront. Today, Siri’s AI performance clearly lags, and Apple is clearly abusing their ability to be late-to-market, but by the end of the decade, they will still turn out to be the Turtle compared to the other two Hares. Both Meta’s glasses and the Jony Ive|OpenAI device collaboration are wildcards in this calculus, but getting lost in that speculation will not help us mere mortals and our careers survive the growth of AI.

The upcoming AI agent battle is a fight to increase our productivity, first in our personal lives and then at work. Just as Amazon started with overturning book stores, the early giant AIs have quickly turned over search. That search doesn’t yet directly compete with iOS apps to stream licensed content or deliver meals and goods, but that will all start to happen before the end of 2025. Everything Machine agents will depend on their ecosystem participants to customize services and apps to make up for the limitations of their agent capabilities. It’s SEO all over again – Google’s search would suck if it were not for the millions upon millions of web publishers that work to optimize their sites for the legacy search crawlers and indexing infrastructure. Operator and Astra require similar cooperation, this time to navigate or evade commerce forms, captchas, and other human-centric features.

How the Everything Machines now deliver data – and will soon deliver dinner – to us is additionally compute-intensive and can most reasonably (queue the Angry Nerds again) be represented as three buckets. The least ambitious, yet still amazing, is Inference, in which the AI interpolates within its existing training set to surface historical statistics, facts, and similar. Next up is GenAI, during which the AI extrapolates from its training set to write our school essays or create images that no human has ever drawn. Third and last, so far at least, are the agents whose main function is to interact with third-party internet systems to execute on some productive set of actions. OpenAI Operator and Google Project Astra get all the press attention, but the Everything Machines’ web crawlers are already agents assigned tasks by the core AIs that are being operated at scale. Operator and Astra will represent radical increases in scope, but there are already impressive agent operations at work today.

There are three differentiated aspects of Apple's approach. First, Apple’s “main” model is three billion parameters, rather than OpenAI’s 1.7 trillion, and resides on the phone. They certainly have lots going on in data centers behind the scenes, but it's nothing compared to their AI competition. Apple’s Everything Machine will be able to update every night on our bedside tables while we sleep using computer chips that are a source of margin rather than capital expense. Those chips are significantly their own IP, not NVIDIA’s. Second, Apple need not crawl the internet gathering the world’s information, organizing it at incredible cost, and hoping to distill it in some way that satisfies some random AI consumer. We have all spent the last fifteen years downloading iOS apps, configuring them, and filling them with private data that Apple can uniquely keep in the individualized AI that will live in Apple Intelligence on each of those phones.

The third difference is both a Carrot and a Stick for Apple and iOS apps. The Carrot is obvious – iOS app providers share Apple’s great incentive to keep as much consumer engagement within the phone as possible. Consumers’ core smartphone usage is concentrated on so few apps per person that those iOS app providers can afford to do a lot more work for Apple than they can for OpenAI and Google. And they are doing so, or will start very soon (Message Prashant or me – our core practice is helping with this) or they will see Apple hand their app share to a more enthusiastic partner. The Stick is the result of a rare self-conscious decision by Apple to let a third party, ChatGPT,  into one of its core consumer workflows. It shows just how big a deal this whole AI hegemony is. With Apple Intelligence, ChatGPT is currently the backfill for Siri. When Siri encounters questions it struggles with, it can pass it along to ChatGPT. For example, when asked for the current dew point, Siri politely suggests passing on that query to ChatGPT. Yet that information exists in Apple’s Weather app and every other weather app that may be installed on an iPhone. If Apple Intelligence, Siri, and iOS apps  fail consistently, ChatGPT will be offered the consumer engagement and transactions that iOS and Apple Pay would otherwise capture. This drives Apple to put  tremendous pressure on app providers to make their data available to Apple Intelligence and jointly fend off ChatGPT.

This world is consistently conquered by Faster, Better, Cheaper -- Pick any Two; Apple owns Cheaper and likely Better. They also know that they have let the wolf into the henhouse and what that means if they do not execute.