22100/12000000 = 0.0018416̅6̅. Thats the amount of musicians that made over $50k in royalties from spotify in 2024. There are so many artists (12 million) who are pursuing their creative passions (in reality its much more that aren’t on the platform), but aren’t being compensated enough to make a living.

most would point to the platform and labels squeeze all the revenue for themselves. this is why taylor swift wanted to separate from her label and pursue her independence, and why kanye, jayz, madonna etc created their own streaming company. It’s the same reason nebula was spun up by youtube creators: they were tired of losing nearly half of their potential revenue. patreon - headed by my fav cover band pomplamoose/scary pockets leader jack conte – as a last ditch resort to find new ways to compensate creators (akin to donationware in oss). The worst offender platforms like Meta and Google take 99%+ of all revenue – you can see just how much these platforms extract in the appendix.

however – all of this is about to break. implode. a massive bubble is here. and im not sure we realize what it means for the future of the social and economic contracts that have been at the base of how the internet works since its inception.

The first internet banner ad was bought by AT&T in 1994. This ad appeared on the website HotWired, which was the online version of Wired magazine. It marked the beginning of online advertising, a pivotal moment that has since evolved into a massive industry (~73% of $1.1T, 9% CAGR, most of which is going towards ‘the big five’, aka Big Tech). In 2017 it was already “massive” at $83B and 16% growth rate.

This digital advertising model, which powers ‘the big five’, is about to fking explode.

the incentive structure of internet has been driven by the handshake promise that google can use my content in return for traffic to my website where i can thereafter sell goods / services. we would add a robots.txt and cookies.txt that allow part of my website to be scraped and users to be tracked so that google could list me on their discovery engine, since navigating every website would be too cumbersome for a user. you can of course replace google with any of the other big five: meta, amazon, microsoft, and alibaba.

the idea is that once a google user is on my website, i could upsell them for other goods/services, like bundling nike shoes and socks for ‘marathon personas’, or insurance premiums for your home, roof and pet frog. this is the unwritten rule of loss leaders, hero products and accenture’s methodology of getting a foot in the door.

Brands pay exorbitant amounts to rank at the top for keywords like shampoo, Chinese food, and accounting firms. Companies that didn’t join this race often got overshadowed by those that did, as seen with the success of Zappos in online shoe retail, NYT with SEO headlines, and TripAdvisor in tourism. Much more on this later

Figure: 96.2% of all clicks are on one of the top 10 links.

in the future there is no reason for the customer to continue onto the website once theyve retrieved their answer. this is the catalyst for the implosion.

we are getting the answer and then moving on.

is this inevitable? what is required to enable this? what does it mean for the future of commerce, advertising and user experiences? Is there a generational startup opportunity? I’m not sure, but I’d like to find out.

history

In the beginning there was nothing. big primordial void. im gonna skip ahead a couple years past the grunting apes to when we codified a set of sounds to represent observed objects and experiences. Functionally, we were communicating (transferring) information: nearby danger, territorial claims, etc. This happened mostly orally, with some hand waving and a lot of bonking.

To no ones modern surprise, the scalability of conveying information to the entire population orally is infeasible within a life time. some bloke was fed up with managing record keeping through a game of telephone and decided to write onto a slab of rock the Kish tablet. Writing is important because it unlocked the rate at which information could be consumed. 2(+) people could read it at the same time. at any point in the day. without blud even being there. plus the information was (literally) set in stone - no one could mishear things.

However, writing itself had a bottleneck: duplication. To reach a wider audience, written material had to be manually copied or moved, which limited the dissemination of ideas. for a couple thousand years, ppl would just show up to the town square or have many scribes reproduce the works - no wonder so many religious monks had commentaries on the commentaries. The invention of the printing press unlocked duplication by stamping the same pages over and over. This is particularly important because (until 1440 - tho china figured it out in the 800s) only powerful institutions with the manpower could disseminate (and contril) ideas. to no ones postmodern surprise, the second major use of the printing press was porn.

Ok now we got consumable information, duplicatable at close to 0 marginal cost. the next bottleneck was
distribution: getting written information into the hands of the public was still a complex task. steam-powered printing presses drastically reduced the cost of printing and enabled newspapers to produce large volumes of copies at a lower price. The “Relation aller Fürnemmen und gedenckwürdigen Historien” in Germany, and the “Gazette” in France, provided regular updates on news, events and information to the masses.

The bottleneck of distribution gradually got less restrictive until the internet made distribution virtually free and accessible to everyone. Additionally, it was global. There were no physical or geographical limitations anymore. This is a major crux of this manifesto: infinite distribution + commoditization = personalization.

Before we go any further, there is an important milestone that I jumped over. Between the written mediums of newspapers and the internet, there existed (still exists?) the proliferation of ideas and information through radio and television. This is very very noteworthy because (for the first time outside of church) we would hear (and eventually see) information directly from its source, immediately. no next day paper. now.

There still remains two challenge: the creation and substantiation of ideas. while I can have numerous ideas and the Internet allows me to distribute them globally, I still need to write them down, just as artists need to create images and musicians need to compose songs. It is becoming increasingly evident that this bottleneck is also on the brink of being eliminated. Ill get to this later, but tldr: AI.

the history of ads

One of the earliest known ads was found in Thebes, Egypt, promoting a reward to capture and return a runaway slave named Shem. Since then, we have paid a very heavy price.

During the 1500s, print ads mainly focused on promoting events, plays, concerts, and products like books and medicines.

The first newspaper advertisement was published on April 17, 1704, in the Boston News-Letter, promoting an estate for sale. In 1741,
none other than the face of the $100 bill himself, pioneered magazine advertising in his ‘General Magazine’ booklet. Ben, frankly, this was a founding moment. In 1835, the very first billboard advertisement was created by Jared Bell in New York to promote Barnum & Bailey Circus.tbt to when zoos were cool and exotic

In 1841, Volney B. Palmer, the first ad agency, was established in Philadelphia. Instead of having to sell ads and build relationships with multiple newspapers, advertisers could work with the ad agency, which could leverage its connections with all those newspapers and serve multiple clients.

Radio gained significant popularity during and after World War II, but the first paid radio ad aired on August 22, 1922, on the New York City radio station, WEAF. The 15-minute ad, paid for by a real estate company called the Queensboro Corporation, promoted apartments in Jackson Heights, Queens. Later, the first television commercial aired in 1941 by the Bulova Watch Company. You can watch it here. It was ten seconds long and viewed by ~4,000 people in New York (#viral?).

The usefulness of ad agencies only grew as more advertising formats like radio and TV emerged. Particularly with TV, advertisers not only needed to place ads but also required significant help in creating them; ad agencies invested in ad-making expertise because they could apply this expertise across multiple clients. This led to the Golden Age of Advertising, where businesses made substantial investments in advertising to convey their brand’s uniqueness and engage their target audience.

Simultaneously, advertisers were rapidly expanding their geographic reach, especially after the Second World War; naturally, ad agencies expanded their reach as well, often through mergers and acquisitions. The primary business opportunity remained the same: provide advertisers with a one-stop shop for all their advertising needs.

Years later, after the development of ARPANET (1960s) and the World Wide Web (1989), the first online display ad was created in 1994 by AT&T, which asked users, “Have you ever clicked your mouse right HERE?” with an arrow pointing to text that read “YOU WILL.” The ad achieved a click-through rate of 44% - a figure that would astonish modern-day marketers. Fitting that the first ad was clickbait.

but why do ads actually work???

When the Internet came along, with its infinite distribution to infinite users, the newspapers, brands, and ad agencies were pumped af: more users = more money! Ad agencies in particular were frothing as more channels means more complexity and the ad agencies could abstract that away for their clients!

Before the Internet, a newspaper like the New York Times was limited in reach; now it can reach anyone on the planet! The problem for publishers, though, is that the free distribution provided by the Internet is not an exclusive. It’s available to every other newspaper as well. Moreover, it’s also available to publishers of any type. In the early internet (and to this day) individuals could post their own news updates, and to the indifferent HTML protocol, it was on ‘completely equal footing’.

That abundance of publications (blogs, webpages, brandsites, etc) meant that discovery was far more important than distribution.

So, two kids in a garage in palo alto decide to invent a way to rank pages, aptly called PageRank. Actually, much like the printing press, the groundwork was done in china. The first successful strategy for site-scoring and page-ranking was link analysis: ranking the popularity of a web site based on how many other sites had linked to it. Robert Li’s RankDex was created in 1996, filed for patent in 1997, granted in 1999, and then he used it to found Baidu in 2000. Larry Page referenced Robin Li’s work in the citations for PageRank.

Increasingly users congregated on two discovery platforms: Google for things for which they were actively looking, and Facebook for entertainment.

IRL

Start with the top 25 advertisers in the U.S. The list is made up of:

4 telecom companies (AT&T, Comcast, Verizon, Softbank/Sprint)
4 automobile companies (General Motors, Ford, Fiat Chrysler, Toyota)
4 credit card companies (America Express, JPMorgan Chase, Bank of America, Capital One)
3 consumer packaged goods (CPG) companies (Procter & Gamble, L’Oréal, Johnson & Johnson)
3 entertainment companies (Disney, Time Warner, 21st Century Fox)
3 retailers (Walmart, Target, Macy’s)
1 from electronics (Samsung), pharmaceuticals (Pfizer), and beer (Anheuser-Busch InBev)

Before going into Google and Facebook’s operating models more deeply, lets look to a more simple analog example. Buying the swiffer (Procter & Gamble’s cleaning mop stick thingy) in a Target. why do we decide to do so?

Ad agencies would say it is due to successful guidance through the Purchase Funnel, a concept initially introduced in 1925 in Edward Strong’s book, The Psychology of Selling and Advertising. The funnel is composed of: “attention, interest, desire, action, satisfaction”.

The real credit goes to E. St. Elmo Lewis, who in 1898 introduced the slogan, “Attract attention, maintain interest, create desire,” during an advertising course he taught in Philadelphia. He mentioned that he derived the idea from studying the psychology of William James. Later, he expanded the formula to include “get action.” Around 1907, A.F. Sheldon further enhanced it by adding “permanent satisfaction” as a crucial element. Although few in 1907 recognized the importance of this last phrase, today it is widely acknowledged as essential for providing service, gaining the buyer’s goodwill, meeting their needs, and ensuring lasting satisfaction.

Anyway,
- Attention: make the buyer aware of a problem they have
- Interest: the buyer becomes interested in solving their problem
- Desire: the buyer becomes interested in your solution to their problem
- Action: the buyer acquires your solution

A classic example of the complete AIDA model condensed into a single commercial is this roll-out campaign for the Swiffer mop

The entire funnel is encapsulated in this ad:
- Attention: Traditional cleaning methods stir up dirt
- Interest: Dirt needs to be removed, not just moved
- Desire: Swiffer cloths collect dirt and can be thrown away
- Action: Find Swiffer in the household cleaning aisle

The reference to the aisle is crucial: shelf space has long been the cornerstone for large consumer packaged goods companies. Swiffer achieved widespread distribution immediately upon launch because P&G could leverage its other popular products during negotiations with retailers like Walmart and Target to secure the best shelf location placement. Importantly, simply being on shelves increases the likelihood that customers will discover you independently or recognize you after repeated exposure. in ‘traditional retail advertising’ shelf space provided both distribution and discovery.

Notice that the vast majority of the industries on the list are dominated by massive companies that compete on scale and distribution. CPG is the perfect example: building a “house of brands” allows a company like Procter & Gamble to leverage scale to invest in R&D, reduce the cost of products, and target demographic groups without perfect personalization. TV is the perfect medium to cater to the masses. In fact, the top 200 advertisers in the U.S love TV so much that they make up 80% of television advertising, despite accounting for only 51% of total advertising spend (and 41% of digital).

Linear television and its advertisers were fundamentally based on controlling distribution (i.e. retail shelf space) and thereby controlling customers.

Many of the companies on this list are now under threat from the Internet. When supply was limited by physical space, shelves were highly valuable for both discovery and distribution. But with the Internet making shelf space virtually limitless, these brands can no longer monopolize the once-scarce shelf space. and I’m no mathematician, but it’s clear that trying to dominate an infinite space is a losing battle. Specifically:
- CPG companies face threats on two fronts: on the high end, the combination of e-commerce and highly-targeted, highly-measurable Facebook advertising has led to a rise in boutique CPG brands offering superior products to very specific groups. On the low end, e-commerce not only diminishes the shelf-space advantage but also sees Amazon making significant moves into private label products.
- Similarly, big box retailers that offer little beyond availability and low prices are being surpassed by Amazon in both areas. In the long term, it’s difficult to see how they will continue to survive.
- Automobile companies, on the other hand, are dealing with three distinct challenges: electrification, transportation-as-a-service (like Uber), and self-driving cars. The latter two, in particular (and to some extent the first), suggest a future where cars become mere commodities purchased by fleets, making advertising to customers obsolete.

Many of these major companies, consciously, unconsciously, or under the veil of ‘Hate Speech’, decided to boycott Facebook in 2020. Household brands like Coca-Cola, J.M. Smucker Company, Diageo, Mars, HP, CVS Health, Clorox, Microsoft, Procter & Gamble, Samsung, Walmart, Geico, Hershey and 1000 others decided to pull their spending on the app. Seems like a major blow, right? Zuckerberg was unphased.

For reference, 2019 Facebook’s top 100 advertisers made up less than 6% of the company’s ad revenue. Most of the $69.7 billion the company brought in came from its long tail of 8 million advertisers. This explains why the news about large CPG companies boycotting Facebook is, from a financial perspective, simply not a big deal.

Unilever’s measly $11.8 million in U.S. ad spend, to take one example, is replaced with the same automated efficiency that Facebook’s timeline ensures you never run out of content. Facebook loses some top-line revenue – in an auction-based system, less demand corresponds to lower prices. And yet, due to these lower prices, smaller direct-to-consumer companies can now bid and steal customers from massive conglomerates like Unilever. The content will be filled regardless, and the markets are very efficient.

The unavoidable truth is that TV advertisers are 20th-century entities: designed for mass markets, not niches, designed for brick-and-mortar retailers, not e-commerce. These companies were built on TV, and TV was built on their advertisements. While they are currently supporting each other, the decline of one will accelerate the decline of the other. For now, the interdependence of these models is keeping them afloat, but it only means that when the end comes, it will arrive more quickly and broadly than anyone anticipates.

Online

the traditional marketing funnel made sense in a world where different parts of the customer journey happened in different places — literally. You might see an advertisement on TV, then a coupon in the newspaper, and finally the product on an end cap in a store. Every one of those exposures was a discrete advertising event that culminated in the customer picking up the product in question in putting it in their (literal) shopping cart.

One of the hallmarks of the Internet is that the entire [AIDA] funnel can be often compressed into a single Facebook ad that you might only see for a fraction of a second; perhaps something will catch your eye, and you will swipe to see more, and if you are intrigued, you can complete the purchase right then and there. The journey is increasingly compressed into a single impression: you see an ad on Instagram, you click on it to find out more, you login with Shop Pay, and then you wonder what you were thinking when it shows up at your door a few days later. The loop for apps is even tighter: you see an ad, click an ‘Install’ button, and are playing a level just seconds later. Sure, there are things like re-targeting or list building, but by-and-large Internet advertising, particularly when it comes to Facebook, is almost all direct response. You might even forget about your purchase right up until a mysterious package shows up at your door a few days later.

Due to the format of these ads, digital advertising has worked much better for direct response marketing. Aka impulse purchases. Notice how this is a very different commerce behavior than having gone to Target, motivated by a TV ad + newspaper coupon, to examine the physical usefulness. Now:
- Facebook helps find the customers
- Shopify or WooCommerce build the storefronts
- Stripe or PayPal handle payments
- Third-party logistics providers package and ship the goods
- USPS, Fedex, and UPS deliver the actual packages

In the same way that the internet made the distribution and duplication of content indiscriminantly, facebook ads allows anyone to put an ad that can reach anyone. This has the same effect of creating perfect competition.

Here is the problem for DTC commerce: Facebook really is better at finding brands’ customers than anyone else. This alludes to Zuck’s endgame, but essentialy DTC ad budgets are forced onto facebook as it is the best (measured) return-on-investment for acquired customers on Facebook, where DTC companies are competing against all of the other DTC companies and mobile game developers and incumbent CPG companies and everyone else for user attention. That means the real winner is Facebook, while DTC companies are slowly choked by ever-increasing customer acquisition costs. Facebook is the company that makes the space work, and so it is only natural that Facebook is harvesting most of the profitability from the DTC value chain.

What made the Facebook model work is that the Meta Pixel (now CAPIs) could map a conversion (downloading of an app/purchasing the swiffer) to ad-targeted customers on their social media app. because Facebook knew a lot about someone who saw each ad and thereafter converted, they created an algorithm in 2013 that could easily find other people who were similar and show them similar ads (lookalike audiences). Along with the news feed, this was one of the biggest moments in Facebook’s history. From then on, they could use the data flywheel to continually optimizing their targeting and increasing their understanding along the way. Now Advantage+ is essentially a 1-click “set it and forget it”.

This has fundamentally changed the plane of competition: no longer do distributors compete based upon exclusive supplier relationships, with consumers/users an afterthought. Instead, suppliers can be commoditized leaving consumers/users as a first order priority. By extension, this means that the most important factor determining success is the user experience: the best distributors/aggregators/market-makers win by providing the best experience, which earns them the most consumers/users, which attracts the most suppliers, which enhances the user experience in a virtuous cycle.
Ben Thompson, Aggregation Theory (2015)

To be fair to the DTC companies, they are hardly the first to make this mistake: when the internet newspapers looked at the Internet and only saw the potential of reaching new customers; they didn’t consider that because every other publisher in the world could now reach those exact same customers, the integration that drove their business — publishing and distribution in a unique geographic area — had disintegrated. For a (long) time, Newspapers dominated both editorial and advertisements, but it turns out that was simply a function of who owned printing presses and delivery trucks; once the Internet came along advertisers, which cared about reaching customers, not supporting journalists, switched to Facebook and Google, which had aggregated the former and commoditized the latter. It is the same lesson that TV-era brands are facing now that they can’t just win by dominating shelf space. If some part of the value chain becomes free, that is not simply an opportunity but also a warning that the entire value chain is going to be transformed. How will brands adapt?
https://stratechery.com/2020/email-addresses-and-razor-blades/

Facebook, Amazon, Netflix, and Google (plus Uber/Airbnb etc) are structurally very similar companies: all leveraged zero distribution costs and zero transaction costs to own users at scale via a superior experience that commoditized suppliers and let them skim off the middle, either through fees, subscriptions, and/or ads.

Google, though, has a built-in advantage: Google doesn’t have to figure out what you are interested in because you do the company the favor of telling it by searching for it. The odds that you want a hotel in San Francisco are rather high if you search for “San Francisco hotels”; it’s the same thing with life insurance or car mechanics or e-commerce.

Amazon goes a step further and has effectively integrated the entire e-commerce stack when it comes to the distribution of goods consumers are explicitly searching for:
- Customers come to Amazon directly
- Searches on Amazon lead to Amazon product pages or 3rd-party merchant listings that look identical to Amazon product pages
- Amazon handles payments
- Amazon packages and ships the goods
- Amazon increasingly delivers the actual packages

This is great for aggregators like Google and Amazon, but not so great for P&G: remember, dominating shelf space was a core part of their strategy, and Amazon and Google have infinite shelf space! Anyone can publish, and this creates perfect competition - terrible for suppliers who face commoditization and shrinking margins, but great for consumers who enjoy more choices and lower prices.

gaming the algorithms

There are two big challenges when it comes to winning search:
- Because search is initiated by the customer, you want that customer to not just recognize your brand (which is all that is necessary in a physical store), but to also recall your brand (and enter it in the search box). This is a much stiffer challenge and makes the amount of time and money you need to spend on a brand that much greater.
- If prospective customers do not search for your brand name but instead search for a generic term like “laundry detergent” then you need to be at the top of the search results. And, the best way to be at the top is to be the best-seller. In other words, having lots of products in the same space can work against you because you are diluting your own sales and thus hurting your search results.

In order to survive on the Internet, an ugly truth was emerging. One has to cater to Google. They have all the customers. Yelp and countless other sites depend on Google to bring them web traffic — eyeballs for their advertisers.

Yelp, like many other review sites, has deep roots in SEO — search-engine optimization. Their entire business was long predicated on Google doing their customer acquisition for them. This meant a heavy emphasis on both speed and SEO, and an investment in anticipating and creating content to answer consumer questions. This includes using tools like SemRush and hiring SEO-specialized ad agencies to flourish online. And to their credit, since their founding in 2004, they’ve had great success in being the destination website for restaurant reviews and the sort.

You may think, are does anything even change if you play the SEO game? The answer is a big-time yes. Even a small boost at the scale at which google operates (13.7B searches/day, 5T/year), would be huge.

Google Search Feature	Click Through Rate (CTR)
Ad Position 1	2.1%
Ad Position 2	1.4%
Ad Position 3	1.3%
Ad Position 4	1.1%
Search Position 1	39.8 §
Search Position 2	18.7% §§
Search Position 3	10.2%
Search Position 4	7.2%
Search Position 5	5.1%
Search Position 6	4.4%
Search Position 7	3.0%
Search Position 8	2.1%
Search Position 9	1.9%
Search Position 10	1.6%

• § If snippet, then 42.9%; If AI overview, then 38.9%; If local pack present, then 23.7%
• §§ If snippet, then 27.4%; If AI overview, then 29.5%; If local pack present, then 15.1%
  Source: First Page Sage

But the data is more than just a small boost - the jump from 18.7% to 27.4% CTR is massive. For most companies, going from search position 8 to search position 3 (or vice versa) can make or break a business.

But AI Overviews (AIO) are existential threats to companies like Yelp. Look at what Chegg wrote in their 2024 Annual Financial Report:

While we made significant headway on our technology, product, and marketing programs, 2024 came with a series of challenges, including the rapid evolution of the content landscape, particularly the rise of Google AIO, which as I previously mentioned, has had a profound impact on Chegg’s traffic, revenue, and workforce. As already mentioned, we are filing a complaint against Google LLC and Alphabet Inc. in the U.S. District Court for the District of Columbia, making three main arguments. We allege in our complaint, Google AIO has transformed Google from a “search engine” into an “answer engine,” displaying AI-generated content sourced from third-party sites like Chegg. Google’s expansion of AIO forces traffic to remain on Google, eliminating the need to go to third-party content source sites. The impact on Chegg’s business is clear. Our non-subscriber traffic plummeted to negative 49% in January 2025, down significantly from the modest 8% decline we reported in Q2 2024.

Theyre not the only one, Stack Overflow has taken a massive hit too. These companies relied on the handshake promise that google can use my content in return for traffic to my website where i can thereafter sell goods / services. Or at the very least display banner ads on their website.

In a 2014 video that Yelp put out, while it makes many of the same arguments as the Chegg lawsuit, instead of being focused on regulators it is targeting Google itself. They argue that Google isn’t living up to its own standards by not featuring the best results, and not driving traffic back to sites that make the content Google needs (by, for example, not including prominent links to the content filling its answer boxes; Yelp isn’t asking that they go away, just that they drive traffic to 3rd parties). Google may be an aggregator, but it still needs supply, which means it needs a sustainable open web.

Yelp, Chegg and Stack Overflow are not the first and not the last to be at the whims of Google. Back in 2006, Belgian news publishers sued Google over their inclusion in the Google News, demanding that Google remove them. After winning the initial suit, Google dropped them as demanded. Then the publications, watching their traffic drop dramatically, scrambled to get back in. When they returned, they made use of the exact opt-out mechanisms - Google offers clear guidelines for publisher’s who do not want to be listed, or simply do not want content cached (robots.txt), which they could have used at any time. They never had to sue; there were mechanisms in place where they could opt-out.

In 2014, A group of German publishers started legal action against the search giant, demanding 11 percent of all revenue stemming from pages that include listings from their sites. In the case of the Belgian publishers in particular, it was difficult to understand what they were trying to accomplish. After all, isn’t the goal more page views (it certainly was in the end!)? The German publishers in this case are being a little more creative: like the Belgians before them they are alleging that Google benefits from their content, but instead of risking their traffic by leaving Google, they’re instead demanding Google give them a cut of the revenue they feel they deserve.

The obvious reaction to this case, as with the Belgian one, is to marvel at the publisher’s nerve; after all, as we saw with the Belgians, Google is the one driving traffic from which the publishers profit. “Ganz im Gegenteil!” say the publishers. “Google would not exist without our content.” And, at a very high level, I suppose that’s true, but it’s true in a way that doesn’t matter, and understanding why it doesn’t matter gets at the core reason why traditional journalistic institutions are having so much trouble in the Internet era.

The ugly truth, though, as these newspaper publishers found out, is that not being in Google means a dramatic drop in traffic. No website can afford to exclude Google’s crawler from robots.txt because it would be economically ruinous. AI Overviews snippets in search (Google’s defense against AI-chatbot incursions into their search dominance) requires that if you want your content available to Google Search — and any publisher must — then your content will go into Google’s most important AI product as well.

Not everyone wants to play into Google’s game, though. Even when their ad business tanked 30% around the same time snippets were introduced, the New York Times wrote:

We are, in the simplest terms, a subscription-first business. Our focus on subscribers sets us apart in crucial ways from many other media organizations. We are not trying to maximize clicks and sell low-margin advertising against them. We are not trying to win a pageviews arms race. We believe that the more sound business strategy for The Times is to provide journalism so strong that several million people around the world are willing to pay for it. Of course, this strategy is also deeply in tune with our longtime values. Our incentives point us toward journalistic excellence … [yet] our journalism must change to match, and anticipate, the habits, needs and desires of our readers, present and future.

That raises the question as to what are the vectors on which “destination sites” — those that attract users directly, independent of the Aggregators — compete? The obvious two candidates are focus and quality. What is important to note, though, is that while quality is relatively binary, the number of ways to be focused — that is, the number of niches in the world — are effectively infinite; success, in other words, is about delivering superior quality in your niche — the former is defined by the latter.

The transformative impact of the Internet is only starting to be felt, which is to say that the long run will be less about traditional companies adopting digital than it will be about their entire way of doing business being rendered obsolete.

why now

It was a symbiotic relationship. Google benefited from being on THE place to find information. Users benefited by quickly finding what they needed. Websites and content creators benefited with larger audiences, focused and happy users, and a clear-cut path toward success. We all got what we needed out of the relationship.
Source: brob

As long as Google was providing ten blue links, it could serve everyone, no matter their values; after all, the user decided which link to click.

By providing ads alongside those ten blue links, Google deputized the end user to choose the winner of the auction it conducted amongst advertisers; those advertisers would pay a premium to win that auction because their potential customer was affirmatively choosing to go to their site, which meant (1) a higher chance of conversion and (2) the possibility of building a long-term relationship to increase lifetime value.

AI threatens this:
If a user doesn’t have to choose from search results, said user also doesn’t have the opportunity to click an ad, thus choosing the winner of the competition Google created between its advertisers for user attention. Because AI gives an answer instead of links, there is no organic place to put the advertisements, bid in its 4 SERP auctions. It is not that Google is artificially constraining its horizontal business model; it is that its business model is being constrained by the reality of a world where, as Pichai noted, artificial intelligence comes first.

In the world of AI Agents you must own the interaction point, and unfortunately there is no room for ads, rendering both Google’s distribution and business model moot. For Google, both must change for the company’s technological advantage to come to the fore.

Ben Thompson, India and Gemini: Ten Blue Links & The Complicity Framework (2024)

Additionally, when AI reduces the 10 blue links to one answer, if the subject rests on subjective values, the search result can never satisfy everyone, Google risks upsetting a significant portion of demand, weakening its Aggregator position and potentially decreasing its leverage on costs. For example, a WIRED analysis demonstrated that:

despite claims that Perplexity’s tools provide “instant, reliable answers to any question with complete sources and citations included,” doing away with the need to “click on different links,” its chatbot, which is capable of accurately summarizing journalistic work with appropriate credit, is also prone to bullshitting, in the technical sense of the word.
This undermines the trustworthiness of the AI Overview, and thus also information the end user consumes (and could therafter recite).

There are three major open threads here (opportunities?), which we’ll go into more in depth in the sections on AI Web, Human Interfacing and Economic Incentives.

what now?

What exactly is happening rn in search?

Sundar Pichai when asked about search volume in their 2024 Q4 Earnings Call:

On Search usage overall, our metrics are healthy. We are continuing to see growth in Search on a year-on-year basis in terms of overall usage. Of course, within that, AI overviews has seen stronger growth, particularly across all segments of users, including in younger users. So it’s being well received. But overall, I think through this AI moment, I think Search is continuing to perform well. And as I said earlier, we have a lot more innovations to come this year. I think the product will evolve even more. And I think as you make Search, as you give, as you make it more easy for people to interact and ask follow-up questions, et cetera, I think we have an opportunity to drive further growth. Our Products and Platforms put AI into the hands of billions of people around the world. We have seven Products and Platforms with over 2 billion users and all are using Gemini. That includes Search, where Gemini is powering our AI overviews. People use Search more with AI overviews and usage growth increases over time as people learn that they can ask new types of questions. This behavior is even more pronounced with younger users who really appreciate the speed and efficiency of this new format.

This echoes the “we built Google for users, not websites” Eric Schmidt ethos that Google generally adheres to. In Q1 2025 Pichai writes that they have 1.5B monthly active users using AI Overviews.

Ok so if there are no places to put Search Enging Result Page (SERP) ads anymore, but does that mean there’s no place to put ads at all? Of course not.

In the Perplexity pitch deck, Perplexity explains how their ads would show up as a side banner or a sponsored ‘follow up link’. In October, Google rolled out displaying ads alongside the AI answer (also see last week’s launch of NexAD for smaller AI search engines), much like what Perplexity pioneered a few months earlier. CBO Philipp Schindler reports positive results for AI search overview advertising:

First of all, AI overviews, which is really nice to see, continue to drive higher satisfaction and search usage. So, that’s really good. And as you know, we recently launched the ads within AI overviews on mobile in the U.S., which builds on our previous rollout of ads above and below. And as I talked about before, for the AI overviews overall, we actually see monetization at approximately the same rate, which I think really gives us a strong base on which we can innovate even more.
“We do have very good ideas for native ad concepts but you’ll see us lead with user experience…just like you’ve seen with YouTube, we’ll give people options over time, but for this year I think you’ll see us be focused on the subscription.” - pichai

Figure: Source: Alphabet Filings, MBI.*

Although this is true, there is is some obfuscation to this truth. We can see that after the gradual rollout of AIO in Q4, Q1 reigned in $66,886M “approximately the same” as last year’s 61,659M. This is notably much less, though. An 8.47% YoY Growth is similar, sure, but also strictly worse than the previous year’s (61659÷54548) 13.03%.

Not only is it worse, but they’re hiding something very important - Cost Per Click has been increasing quite a bit. They cant do this forever.

Unfortunately, these are temporary fixes to a much larger problem.

Problem 1: human interfacing

Figure: Source: Alphabet Filings, MBI.*

Its one thing to replace human agencnies. Its another to replace human agency.

Yes, many of these ad agencies are built on helping brands like Proctor & Gamble find and market to their customers. Since the inception of the advertisement the goal has been to nudge customers to grow an affinity to and purchase their goods and services. Ads encourage the Attention, Interest, Desire and/or Action of purchasing behavior.

Search ads have always been a great place for advertisement because they catch users at a high intent moment. Meaning, a user is actively looking for something, and if you can provide the solution (according to their taste), theyll pick you!

One of the benefits of having 10 blue links was the choice. Users could be the arbiter of taste. Ads and SEO optimized links were entered into the arena of usefulness and the user could declare the victor. This was particularly relevant for Google, who could use a human’s click as a ranking parameter. Now, when an AI Overview is spitting out the answer, the search engine is the arbiter of taste.

This is problematic for two major reasons: Trustworthiness and Usefulness.

Trustworthiness

Google wants to “make information universally accessible and useful”. So, over the years, they’ve tried a bunch of stuff:

Each feature launch was an incremental improvement for Google’s search users. Search for a local restaurant, get a widget in the sidebar (nice and out of the way) with important information like the phone number, address, and even a link to the website. Nice!

All these changes make sense from the Google Side - Google ads became more important (and thus user engagement became more important), so Google wanted fewer users leaving the search page.

Seeing a 2 on the screen when a consumer enters 1+1 is handy, but it’s not without cost. If someone runs a calculator website, showing the 2 could lead to the calculator website’s traffic decline. Google must, therefore, make tradeoffs. These tradeoffs typically are good for users, but not always. As Eric Schmidt once said, “we built Google for users, not websites.” That’s reasonable, especially when it comes to immutable facts. Giving billions of users a quick answer to 1+1 seems like it outweighs the cost of lost traffic to the calculator website operator. But who decides that?

And what if its not true?!

Yes that seems silly to say when the quick snippet answer to 1+1= spits out 2, everyone can indeed agree it is truthful (fun fact it took Bertrand Russell and Alfred North Whitehead 300+ pages to prove this). But what if the “answer” being served up is best pediatrician mountain view ca?

Google will give you an answer: 10 blue links that point capture 99.37% of clicks. meaning, if you’re not in the top 10 ranked pediatricians, you wont get any business. And so SEO optimization games begin for attention capture and algorithmic blessing. These aren’t actually the best pediatricians in mountain view - theyre the best SEO optimized pediatricians in mountainview. This is untrustworthy in itself sure, but AI Overviews take this to another level because it draws from the gamified blue links and its own intuited world model.

When Google’s AIO came out, there was a HUGE problem with the trustworthiness of its answers. It would suggest putting glue on pizza to stop the cheese from sliding, smoking 2-3 cigarretes/day while pregnant and that it indeed violates antitrust law (wait that one actually might be true). See a funny compilation here. Even within their own official pitch deck, Perplexity’s AI answer includes a hallucination – the Studio Ghibli Museum isnt in Shinjuku, in fact, it isn’t even in Tokyo!

Figure: perplexity is a series Z company.*

The web has a rich, competitive offering of services to help answer such a question, yet these search engines give themselves exclusive access to the prime real estate of the page, proclaiming ‘the best answer’ in its answer snippet. By doing this, user’s choices are being made for them. Your choice.

Are you worse off?

Its one thing to replace human agencnies. Its another to replace human agency.

Usefulness

One could argue that a 100% brand takeover is fine if its useful. Lets say you search for a hotel in shinjuku, and the hilton takes over. What percentage of people would just ‘go with it’, without further research? Taking it a step further, what if the there is a One Click 🪄✨ Button?

If your answer (/intuition) is any greater than the % that wouldve selected the same hotel after self discovery, we’ve lost agency, obviously. But usefulness? Harder to measure, but we can look at the counterfactual. If people search through the ten blue links until they find something they are content with, navigating all the levers (price, distance, availability, (subjective) quality, etc), and the distribution of purchases of hotels is any different than that with a brand takeover, it means that some people didnt get their first choice. They were nudged into a suboptimal choice. In my personal opinion, this is evil.

In April 2024, Chief Business Officer Dmitry Shevelenko told Digiday about the plans for a road map focused on “accuracy, speed and readability.” Shevelenko added that the company didn’t plan to rank sponsors differently than organic answers. “The weighting will be the same. That’s our version of ‘Don’t Do Evil,’” Shevelenko told Digiday earlier this year. “We’re not going to buy us an answer. Whenever we have advertising, that’s the one thing we don’t do: You can’t pay us to have the answer be different because you’re paying us.”

That certainly makes sense for Perplexity, which has one other important factor working in their favor: relative to Google, Perplexity doesn’t have any revenue to lose. ChatGPT similarly claims it wont let you ‘buy an answer’:

Figure: what happens when u usurp your core operating model.*

Yet its happening. Google announced a few days that it would pervasively display AIO ads. When does a banner ad taking up X% of the screen start having serious impacts on the freedom of choice? 1%->3%->10%->50%->100%? What about traffic to the websites?

The first customers will be major brand advertisers who are both not necessarily looking for a click, but rather to build brand awareness and affinity, and who have infrastructure in place for measuring that (and, helpfully, they have experimental budgets that Perplexity can tap into for now). Brands that have strict brand guidelines, know what keywords to omit and have the bandwith to write manual ‘Sponsor Related Questions’. By blowing large budgets on AI Overviews, these Fortune 200s can not only build consumer Awareness and Interest, but buy Actions that wouldn’t have otherwise taken place. It shouldnt be a surprise either, that these companies are more price inelastic when the search engines increase CPC.

On their pitch deck, Perplexity boasts a 46% Follow-up Question rate. They highlight this because it is one of the opportunities for ad placement, where you can place a website that encourages discovery (e.g. ‘best running shoes -> best running shoes for a marathon’). But to me that screams ineffectiveness: 46% of the time, the user is coming to the platform and not getting the answer theyre looking for (sure, yes, some percentage is discovery, but not all of it). As mentioned before, this points to the ugly truth that for most questions, one search result can’t satisfy everyone. It just isnt useful enough.

Its important to reiterate that with a single answer, we not only reliquishing agency of our choices to The Big Five, but get a suboptimal answer meant to satisfy everyone. In an AI-first world where “we are getting the answer and then moving on,” the multi-visit discovery phase vanishes, collapsing the critical revenue stream for both Google and content publishers. These problems are dialed up to 11 when there are no longer any humans browsing.

Problem 2: ai web

Where we started

The World Wide Web (1989) was built by humans, for humans, and it started with written text, because of course, we started with written text for distribution to the masses. As people began hyperlinking to each other’s websites, the web quickly became cluttered and ‘unparsable for humans’. Don Norman, one of many researchers exploring how people interact with computers in the 1990s, coined “User Experience” (UX) - a shift from focusing solely on aesthetics and/or functionality to considering the user’s needs and emotions.

Google’s subsequent intervention (drawing on Robin Li’s work) was a welcomed relief, bringing us back to human-legible designs. As we know from earlier, they iterated quite a bit to make information universally accessible and useful.

But things changed when agents began browsing the web.s

Figure: Source: Cloudflare Data Explorer *

The Internet Archive announced back in 2017 that their crawler would ignore a widely accepted web standard known as the Robots Exclusion Protocol (robots.txt):

Over time we have observed that the robots.txt files that are geared toward search engine crawlers do not necessarily serve our archival purposes. Internet Archive’s goal is to create complete “snapshots” of web pages, including the duplicate content and the large versions of files. We have also seen an upsurge of the use of robots.txt files to remove entire domains from search engines when they transition from a live web site into a parked domain, which has historically also removed the entire domain from view in the Wayback Machine. In other words, a site goes out of business and then the parked domain is “blocked” from search engines and no one can look at the history of that site in the Wayback Machine anymore. We receive inquiries and complaints on these “disappeared” sites almost daily… We see the future of web archiving relying less on robots.txt file declarations geared toward search engines, and more on representing the web as it really was, and is, from a user’s perspective.

Maybe you can give them a pass, being a non-profit and all. But when these big AI companies started ignoring robots.txt, despite claiming that they don’t, things get complicated. Robb Knight at WIRED observed a machine — more specifically, one on an Amazon server and almost certainly operated by Perplexity — doing this on WIRED.com and across other Condé Nast publications. This, paired with clear regurgitations of NYT content by ChatGPT sparked a frenzy of lawsuits.

Figure: Source: The court case

OpenAI now lets you block its web crawler from scraping your site to help train GPT models. OpenAI said website operators can specifically disallow its GPTBot crawler on their site’s robots.txt file or block its IP address. “Web pages crawled with the GPTBot user agent may potentially be used to improve future models and are filtered to remove sources that require paywall access, are known to gather personally identifiable information (PII), or have text that violates our policies,” OpenAI said in the blog post. For sources that don’t fit the excluded criteria, “allowing GPTBot to access your site can help AI models become more accurate and improve their general capabilities and safety.”
Blocking the GPTBot may be the first step in OpenAI allowing internet users to opt out of having their data used for training its large language models. It follows some early attempts at creating a flag that would exclude content from training, like a “NoAI” tag conceived by DeviantArt last year. It does not retroactively remove content previously scraped from a site from ChatGPT’s training data.

Tbh this news isnt a solution. Call me cynical, but that last line explains why: OpenAI has already benefited from all of the data on the Internet, and established a norm that sites can opt out of future AI models, whicih cements the advantage of being a first mover and throws the ladder behind them. Since that announcement, GPTBot hasnt slowed down at all, as shown in the above image on crawlers. The opt out is nice, but its not like most publishers know how to update their robots.txt. Plus, who knows what economic impact of opting out these publishers would face.

its not like its stopping them from displaying the text in AIO either source

History seems to be rhyming with the German and Belgian publishers 20 years ago. Additionally, if these publishers are not increntivized, to continue giving content to these AIs, are we the stuck in the 2010s internet forever?

Keep in mind that it’s unclear that crawling Internet data is in any way illegal; I think there is a strong case that this sort of data collection is fair use, although the final determination of that question will be up to the courts (e.g., Shutterstock, NYT, Sarah Silverman vs OpenAI & friends). What is certain is that OpenAI isn’t giving the data it collected back.

Where we’re going

Beautiful Soup (bs4) and Selenium have been widely used for testing and automating interactions with web applications. These tools have been instrumental in allowing developers to extract data and simulate user interactions (e.g. what happens when you get the Reddit Hug of Death). 🅱️rowserBase took these tools and made them much more scalable and efficient by abstracting much of the complexity involved in setting up and managing browser instances via the cloud, allowing developers to focus on the logic of their automation tasks. One such task can be penetration testing, sure, but lately a new question is emerging.

What happens when AI agents start browsing on our behalf?

Take a moment to watch this agent buy their human a sandwich via 🅱️rowserbase’s headless browser:

Figure: Source: Evan Fenster on X the everything app

Notice how the AI never stopped or clicked on an ad. The agent had a task, and executed it.

This task was simple enough, as the order and supplier were explicit. But what happens when someone asks their AI Agent to “buy them a red sweater”? We’ll explore more of what the solution requires here, but lets focus on the ad problem in this section.

How should google count these impressions? If they stepped away to brew some coffee, would the user even know about any ads on the websites the agent browsed? Should they?

In a similar vein of questioning, Openai and Perplexity have ‘promised’ to never let you buy an answer, but what happens when the AI Overview / first link upon ‘red sweater’ search result is an ad? This very quickly rehashes the earlier problems of trustworthiness and usefulness.

Taking this a step further: remove the web browser interface altogether. Where are you going put the ads? In the GET requests between Shopify and Openai APIs?

There are a lot of questions here with no clear answers. However, their answers in the pre-Agentic Web era were the foundation on which attribution and discovery were fueled. These measurable ‘bounce rates’, ‘time on site’, etc were what Google, Meta, Shopify & friends used to measure the effectiveness and conversions of ads on their platform. Its all about to be uprooted with ‘personal agents’.

The decline of ads is approaching quicker than we think, first with the decline of publishers, and then with the decline of websites altogether.

Problem 3: the incentives

Title	Link	Outcome
Ahrefs Study on CTR Decline	Google AI Overviews Hurt Click-Through Rates	34.5% drop in CTR for top organic positions when AI Overviews are present.
Amsive Research on CTR Impact	Google AI Overviews Hurt Click-Through Rates	Average 15.49% CTR decline across 700,000 keywords; up to 37% drop for non-branded queries.
Raptive Estimate on Traffic and Revenue Loss	Why Publishers Fear Traffic Ad Declines from Google’s AI-Generated Search Results	AI Overviews could reduce publisher visits by up to 25%, leading to $2 billion in annual ad revenue losses.
Swipe Insight Report on Traffic Decline	Google’s AI Overviews Cause Publisher Traffic Decline Estimated $2B Ad Revenue Loss	Some publishers experienced up to a 60% decrease in organic search traffic due to AI Overviews.
World History Encyclopedia Case Study	AI Took My Readers: Inside a Publisher’s Traffic Collapse	Observed a 25% traffic drop after content appeared in AI Overviews.
WARC Study on CTR Reduction	AI Overviews Hit Publisher Traffic Hard, Study Finds	Organic click-through rates dropped to 0.6% when AI Overviews are present, compared to 17% for traditional links.
Bloomberg Report on Independent Websites	Google AI Search Shift Leaves Website Makers Feeling Betrayed	Independent websites have seen significant declines in traffic due to AI-generated answers.
eWeek Analysis on Web Traffic Decline	Google AI Overviews SMB Impact	Some website owners experienced up to a 70% decline in web traffic following AI Overviews introduction.
Northeastern University Insight	Google AI Search Engine	AI Overviews could reduce website traffic by about 25%, particularly affecting web publishers reliant on search traffic.
Press Gazette Warning on Publisher Visibility	Devastating Potential Impact of Google AI Overviews on Publisher Visibility Revealed	AI Overviews could dramatically impact publisher traffic and ad revenue, especially in health-related queries.

The foundational handshake of the internet, where content creators exchange their material for traffic and potential revenue, is being shattered by AI overviews that eliminate the necessity for users to visit original content sites. The many studies above, as well as the many previously mentioned (Chegg, Stack Overflow and Yelp), highlight a 15-35% decline in click-through rates (CTR) when AI Overviews are present, with some publishers experiencing up to a 70% decrease in organic search traffic.

Publishers, once riding high on search engine traffic, are now staring down a harsh new reality: their content is being consumed without any direct interaction. Publishers are missing opportunities to sell their goods and services.

This isn’t just about losing ad revenue; it’s about survival.

If these publishers (bloggers, newspapers, scientific research, how-to articles, etc.) no longer exist, or are less incentivized to create high-quality content due to cost savings, the consequences are dire. The pool of information that search engines rely on would become increasingly scarce, or diminish in quality and diversity. As publishers struggle to monetize their efforts, the incentive to invest in high-quality content creation wanes. This scarcity could lead to a homogenized internet landscape dominated by AI-generated content, which often lacks the depth, nuance, and originality that human creators provide. Ryan Broderick writes in Fast Company: “Why even bother making new websites if no one’s going to see them?”

Figure: Source Cision

As revenue becomes increasingly sparse, its understandable that (though these publishers will never admit to it) LLMs are very much generating content on their platforms. Either as a first draft or final draft, publishers first used the AIs to help produce for their long tail (SKU descriptions, sports coverage, etc). As we get more and more AIs publishing, the Dead Internet conspiracy becomes even more real. ​G/O Media faced social media backlash for using AI to produce articles in 2024, with readers describing the move as “pure corporate grossness” and criticizing the publication of “error-filled bot-authored content.” ​They ended up selling almost all their portfolio newspapers (including The Onion to my mentor Jeff Lawson, hi jeff <3) and laying off much of their staff. Similarly, when CNET published AI-generated articles without clear disclosure, leading to widespread criticism over factual errors and plagiarism. The controversy resulted in staff layoffs, unionization efforts, and a significant devaluation of the brand. In August 2024, Ziff Davis acquired CNET for 100 million USD, a stark drop from its previous valuation ($1.8B).

Quick Math

Ok lets recapulate and determine just how much $ is up in the air.

Lets deconstruct the global digital advertising market into the affected products. In 2023, Google’s share of digital advertising revenues worldwide was projected to amount to 39 percent. Facebook followed with a projected digital ad revenue share of 18 percent, while Amazon came in third with an expected seven percent. The player from Asia with the highest share is TikTok, with three percent, followed by Baidu, JD.com, as well as Tencent, all three with two percent. Lets break that down.

Global digital ad spend hit $1.1 trillion in 2024.

Digital formats account for roughly 72.7% of total ad spend, i.e. $790 billion.

Figure: Source Cision

We can split this into two major spend types: search (SERP) and display (banners, rich media, video).

search ads

Figure: Source Cision

Obviously, all these numbers are estimates but statista claims 40% of digital spend goes to search, emarketer says 41.5%, and oberlo says 41.4% which means its somewhere in that ballpark. lets just go with the conservative numbers → $316 B

2025 search marketshare, has Google: 89.66% Bing: 3.88% Yandex: 2.53% Yahoo: 1.32% DuckDuckGo: 0.84% Baidu: 0.72% Others: 1.05%. But you can’t just distribute the add spend proportionally, since that would omit the other major search platform, retail.

According to eMarketer, 49% of Internet shoppers start their searches on Amazon, and only 22% on Google; Amazon’s share is far higher for Prime subscribers, which include over half of U.S. households.

Putting it altogether:

Search Platform	Ad Spend (2024)	Reference
Google Search (SERP)	$198.1 B	Alphabet 2024 Annual Report
Amazon on-site Search	$56.22 B	Jungle Scout: Sponsored Products ≈ 78 %
Alibaba on-site Search	$25.2 B	Encodify: $42 B; 60%? is on-site search
Walmart Connect Search	$3.5 B	AdExchanger: $4.4 B global ad rev; 80%? on-site search
eBay Promoted Listings	$1.47 B	eBay Q4 “first-party ads” $368 M × 4 quarters
Target	$0.49 B	Marketing Dive: $649 M Roundel rev; 75%? is product-ad/search
Other search engines and retailers (Bing, Yandex, Baidu, etc)	$43.4 B	Remainder
Totals	$316 B

all of this is going to be seriously challenged by the AI Search renaissance.

display ads

Display ads (banners, rich media, video) represent about 56.4% of digital spend → $461.07 billion.

By 2025 programmatic channels will handle 90% of display ad budgets; assuming a similar rate for 2024, that was roughly $414.96 billion in programmatic display spend

Global affiliate marketing spend (performance-based links driving publisher commissions) will exceed $15.7 billion in 2024

Sponsored content (native, branded articles/videos) in the US alone tops $8.14 billion this year

In total, the combined spending on display ads, programmatic channels, affiliate marketing, and sponsored content is ~$438.8 billion in 2024.

Ad tech fees (exchanges, SSPs, DSPs) typically consume ~30% of programmatic budgets. If publishers capture the other ~70%, that implies 438.8B × 0.7 ≈ $307.16 billion in programmatic display‐ad revenue flowing to publishers. The flip of this is the 131.64B in revenue that the ad industry doesn’t get either.

google

to single out google’s AdSense for Content program, publishers keep about 68% percent of the revenue. Which, if Google’s programmatic‐platform market share is 28.6% → 111.5 B, publishers receive ~$75.8 B.

According to Gamob, Google’s search revenue sharing network, The Search Partner (AdSense for Search) represents roughly 10% of google’s total revenue. According to the actual 2024 anual report filing, it was closer to 8.67% ($30.36B). Publishers earn 51% of partner-network ad revenue → $15.18 B. Yandex, Yahoo, etc., offer limited or no comparable search-partner revenue-share programs, so we treat their downstream to external publishers as negligible for this estimate.

If we add google’s 198.08B from search, 30.36B from display and youtube/CTV’s 36.15B, it precisely equates to the 61659+64616+65854+72461=$264.59B in 2024 ad revenue we had in the figure back when we were discussing the slowing 8.47% YoY Growth in ad revenue, mitigated by increasing CPC costs. beautiful. whats gonna happen to it? what about these other companies?

Figure: Source: math + claude viz

where to next?

ok so the economic model of the internet is broken. #genz yay 🙄

the incentives of publishers are no longer to produce high quality content, unless they want to game the algorithm to rank higher in AI Overviews (Generative Engine Optimization) and/or sign a faustian contract to let the algorithms pilage their articles.

when we look for information on the internet, the AI Overviews are not truthful not useful when they give us suboptimal or entirely false answers in the pursuit of ad placements or serving a majority of users.

All that was back when we were using human interfaces - increasingly so, agents are interfacing with the internet on our behalf.

Plus, whats gonna happen to the $316B in search spend? What will Proctor & Gamble gamble on? What will happen to the publishers that are no longer rewarded for web traffic with (~100B in) revenue sharing, and more importantly, can no longer (up)sell their products via their website?

trends

one of the ways to figure out what to do next is to see where things are heading more generally. we should look at what is possible now that previously wasn’t possible due to the advent or convergence of multiple innovations that collectively lead to generational companies. The lineage lines are very clear if you know your history:

Alexander Bell’s advent of the telephone in 1876 came off the convergence of telegraphy (morse code) adoption, electrification progress and the increasingly cheap railroad/wood costs to build poles that could vary the intensity of electric currents to replicate sound waves. With this development of electric signal broadcasting infrastructure, and the utility of instant communicatiton in WW1, broadcasting voice to many users (not just 1-1) was quickly adopted leading to radio’s pervasiveness. 40 years later as cost of producing visual media fell (e.g. photographs instead of paintings), it became more feasible to transmit not just voice but also images over long distances, leading to the first television broadcasts. Early television systems used mechanical methods, such as spinning disks, to scan and display images. With Steven Sasson’s (1975) realization that images could be captured AND stored electronically (by using a increasingly cheap CCD image sensors) he packaged the nobel prize invention with cassete tapes into a toaster sized 8lb portable digital camera.

The Bell Telephone Company established a vast conglomerate and monopoly in telecommunication services until ‘Ma Bell’ was forced to break up in 1982 due to antitrust regulations. This monopoly was based on the pre-allocation of network bandwidth, which involved assigning radio frequencies to different applications. During the Cold War, to prevent the public broadcasting of sensitive data, DARPA initiated the development of ARPANET. ARPANET was built on the foundation of packet-switching, TCP/IP protocols, and IMP routers. This ‘alternative’ to traditional telecommunications was progressively adopted by the university researchers that created it and eventually led to the creation of the web as we know it today. As mentioned earlier, it was important that these data packets sent and received were legible, so Tim Berners-Lee’s developed aa global hypertext system for information sharing. That information, text, voice or the now digial images ‘permitted’ Napster to share music files over the internet from one ‘peer-to-peer’. It was a shortlived movement, from 1999-2001 but revealed the public’s hunger for on-demand, personalized, frictionless access to media, decoupled (decentralized) from traditional broadcast and retail gatekeepers.

But what if, instead of downloading the (music/text/etc) data packets to my floppy disks, I could just host media on the public web and have people stream directly? Great question, YouTube founders. In 2005, as internet bandwidth became capable of file transfers at megabits per second, and Macromedia’s (later, Adobe) 1996 invention of Flash (RIP 1996-2020) enabled interactive websites, simple upload-and-share interfaces democratized video (text/music/etc) publishing while its streaming capabilities eliminated the need for complete downloads before viewing. In 2007, a certain turtlenecked individual merged all of the progress that made PCs more affordable/compute efficient, a digital camera, telephony compliance and internet browsing into (you guessed it) the iPhone. All founders have to mention it at some point, forgive me, but hopefully you can see why its such a landmark moment. Take a moment to bask:

Figure: Source: Evan Fenster on X the everything app

The iPhone revolution, via its app store, permitted social graphs, image sharing and instant news/information sharing that still struggles to satiate the public thirst. Driven by the now vast data published, cloud-distributed computing resources, and rehashed algorithmic research from the 70s, machines could learn patterns (in images, search intent, or entertainment). Its gotten to the point that it can even generate its own entertaining media, or answers to search queries as we very well know by now.

toys

The reason big new things sneak by incumbents is that the next big thing always starts out being dismissed as a “toy.” This is one of the main insights of Clay Christensen’s “disruptive technology” theory. This theory starts with the observation that technologies tend to get better at a faster rate than users’ needs increase. From this simple insight follows all kinds of interesting conclusions about how markets and products change over time.

Disruptive technologies are dismissed as toys because when they are first launched they “undershoot” user needs. The first telephone could only carry voices a mile or two. The leading telco of the time, Western Union, passed on acquiring the phone because they didn’t see how it could possibly be useful to businesses and railroads – their primary customers. What they failed to anticipate was how rapidly telephone technology and infrastructure would improve (technology adoption is usually non-linear due to so-called complementary network effects).

The same was true of how mainframe companies viewed the PC (microcomputer). Initially, PCs were seen as toys for hobbyists, incapable of the kinds of serious business applications that IBM handled. Yet, as software and hardware rapidly improved along the exponential curve of fabs doubling the number of transistors on a microchip approximately every two years, PCs became indispensable tools in both personal and professional settings.

Kodak made a lot of money with very good margins providing silver halide film; digital cameras, on the other hand, were digital, which means they didn’t need film at all. Kodak’s management was thus very incentivized to convince themselves that digital cameras would only ever be for amateurs. Kodak filed for bankrupcy in 2012.

YouTube’s early days featured grainy, amateur videos that serious media companies considered amateurish and irrelevant. “Broadcast yourself” seemed like a frivolous concept when the first videos were cats playing piano and awkward vlogs.

The first iPhone was ridiculed by BlackBerry executives for its poor battery life, virtual keyboard, and limited enterprise features. “It’s a toy for consumers,” panned by the likes of Microsoft’s Steve Ballmer — who laughed off its lack of a physical keyboard.

Early apps were basic utilities and games like “iBeer” (a virtual glass of beer) or keeping the flashlight on. Friendster, MySpace, and Facebook were considered trivial diversions for teenagers — mere digital yearbooks with Farmville and the ability to “poke” each other. Established media companies and advertisers dismissed them as fads, failing to see how these networks would become primary channels for news distribution, customer engagement, and political discourse. Steve Jobs saw right through this, watch this launch of Garageband:

Figure: Source: Evan Fenster on X the everything app

[12:24] I’m blown away with this stuff. Playing your own instruments, or using the smart instruments, anyone can make music now, in something that’s this thick and weighs 1.3 pounds. It’s unbelievable. GarageBand for iPad. Great set of features — again, this is no toy. This is something you can really use for real work. This is something that, I cannot tell you, how many hours teenagers are going to spend making music with this, and teaching themselves about music with this.

A couple years later, facebook left the world wide web to go mobile native. They were pegged as a way to play toy apps and video games. From TechCrunch in 2012:

Facebook is making a big bet on the app economy, and wants to be the top source of discovery outside of the app stores. The mobile app install ads let developers buy tiles that promote their apps in the Facebook mobile news feed (e.g. Farmville, Plants vs Zombies). When tapped, these instantly open the Apple App Store or Google Play market where users can download apps.
Much of the criticism of app install ads rests on obsolete assumptions that view apps as fun baubles instead of the dominant interaction layer between companies and consumers. If you start with the premise that apps are more important than web pages or any other form of interaction when it comes to connecting with consumers, being the dominant channel for app installs seems downright safe.
https://stratechery.com/2015/daily-update-bill-gurley-wrong-facebook-youtubes-competition/

Almost all early efforts of AI were to play recreational games like chess, Go, and poker. OpenAI started by training models to beat DoTA champions. These EXACT same algorithms power website ranking elos, protein folding research, and ChatGPT’s reasoning models. LLMs started with recipes and toy use cases but are maturing into enterprise use cases (starting, of course, with “fresh grad” tasks). the landmark AI Agent paper was about playing minecraft, and you can go watch Claude Play Pokemon right now.

Disruptive innovation is, at least in the beginning, not as good as what already exists; that’s why it is easily dismissed by managers who can avoid thinking about the business model challenges by (correctly!) telling themselves that their current product is better. The problem, of course, is that the disruptive product gets better, even as the incumbent’s product becomes ever more bloated and hard to use. That, though, ought only increase the concern for Google’s management that generative AI may, in the specific context of search, represent a disruptive innovation instead of a sustaining one.

AI

It’s become increasingly clear that AI is not a toy, and will become a defining pillar for the next generation of coalescing trends. We’ll explore a few others in a bit, but lets focus on AI first and foremost.

I’m not going to go into the history of AI because it’s pretty well known at this point and tbh not super helpful for the unfolding of the agentic web. What I will do, is define Artifical as ‘nonhuman’, and borrow the definition of Intelligence from Jeff Hawkins’ theory of the brain in A Thousand Brains: A New Theory of Intelligence. Hawkins writes:

Intelligence is the ability of a system to learn a model of the world. However, the resulting model by itself is valueless, emotionless, and has no goals. Goals and values are provided by whatever system is using the model. It’s similar to how the explorers of the sixteenth through the twentieth centuries worked to create an accurate map of Earth. A ruthless military general might use the map to plan the best way to surround and murder an opposing army. A trader could use the exact same map to peacefully exchange goods. The map itself does not dictate these uses, nor does it impart any value to how it is used. It is just a map, neither murderous nor peaceful. Of course, maps vary in detail and in what they cover. Therefore, some maps might be better for war and others better for trade. But the desire to wage war or trade comes from the person using the map. Similarly, the neocortex learns a model of the world, which by itself has no goals or values. The emotions that direct our behaviors are determined by the old brain. If one human’s old brain is aggressive, then it will use the model in the neocortex to better execute aggressive behavior. If another person’s old brain is benevolent, then it will use the model in the neocortex to better achieve its benevolent goals. As with maps, one person’s model of the world might be better suited for a particular set of aims, but the neocortex does not create the goals.

To the extent this is an analogy to AI, large language models are intelligent, but they do not have goals or values or drive. They are tech tools to be used by, well, anyone who is willing and able to take the initiative to use them.

How do we use tech today? There are two types of tech philosophies: tech that augments, and tech that automates. We’ll get more into that when looking at economic models for success in this new agentic world. But the quick question we should ask ourselves is: will AI be a bicycle for the mind; tech that we use for achieving our goals, or an unstoppable train we dont control that sweeps us to pre-set destinations unknown?

The route to the latter seems clear, and maybe even the default: this is a world where a small number of entities “own” AI, and we use it — or are used by it — on their terms. This is the outcome that has played out with ‘traditional’ AI algorithms like social media and GPS. This is the iphone outcome that Johny Ive claims as his frankenstein. This is the outcome being pushed by those obsessed with “safety”, and demanding regulation and reporting; the fact that those advocates also seem to have a stake in today’s leading models seems strangely ignored.

agents

The algorithms that are used to play chess and video games, are not particularly useful by themselves. The algorithms become much more capable when connected to tooling that allows them to interact with the world. This can be as simple as the functionality of moving a chess piece (digitally), to the complexity of humanoid robots. These AIs can go beyond their sandbox and actually assist on general tasks. With these increased capabilities comes increased responsibility.

An assistant has to be far more proactive than, for example, a search results page; it’s not enough to present possible answers: rather, an assistant needs to give the right answer.
Ben Thompson, Google and the Limits of Strategy (2016)

prophetic words from nearly 10 years ago. we still can’t seem to get there.

As written earlier, the tech not only has to be trustworthy, but also useful. In the agentic world, it isnt sufficient to offer 10 blue links, the UX is that the AI Agent will return with a single answer, trustworthy, useful and correct.

There is a delicate balance between these each of these attributes. For example, trustworthiness can be augmented with encoraging behavior without being to sycopanthic or untruthful if the output suggests cigarretes for a pregnant migraine even if thats what the user wants to hear. This is why correctness protocals are implemented, but overcorrectness can lead to non-useful answers that are overly politically/ethically biased. This gets increasingly muddled when ads are thrown into the AI answer mix.

Without rehashing too much of what has already been raised as qualms, lets look at where agents are headed. Today these agents are mostly the routine, rules based, repitive labor, like form filling and research. The trends of decreasing computation costs, increased algorithmic competency, and pervasive tooling mean that agents will become capable of increasingly complex tasks.

Figure: Source CB Insgights

The tooling for these agents is still in its infancy. Developers are using the scaffolding of Anthropic’s MCP servers, Google’s A2A protocol and OSSs like Langchain to enable their agents to get appropriate context for their tasks, as well as partner with the right services. You can read more about these protocols here: Tyllen on AI Agents.

But tools arent the only thing an agent needs to be successful in their task completion. In order to do what a human would’ve done when presented with the 10 blue links, we need more than just the browser tool and intelligence, we need to translate the user’s intention into a search query, navigate and incorporate the outputs, and structure them in a way useful for the agent and the human it is representing.

We’ll go deeper into intent translation later, as its very much defined by the interface the human contacts its agent through, but it shouldnt be a surprise that there are many startups creating a standardized AgentOS. This started with toy usecases like anthropic’s computer use, and openai’s operator, which currerntly just take a screen shot of the page, feed it into the LLM and execute clicks and keyboard presses through Accessibility mode permissions. This is very skeuomorphic. AgentOS companies want to go beyond controlling windows hardware and just reinvent the web/computer process altogether. RPA may have rode this wave 10 years too early. why open a headless browser on a windows machine to ‘manually’ type a query into a google form when i could just request a service from an agent representing the database of the entire indexed internet?

organizations

tooling is useful for the individual agent, but once agents start communicating with each other, complications arise. Its one thing to establish the communication protocals of Agent to Agent (A2A) with standards like web2’s HTTP/SSE/JSON-RPC (im compiling a list here), but what about the actions they take?

the most important one is probably payment security. How can I trust that the agent is spending real money, and will receive a real service? to save us both some time, I won’t go into the history of payments digitally, but the major companies who solved online purchasing are now turning their heads to pioneering agentic AI systems that enable AI agents to make payments on behalf of consumers. Visa, Mastercard, Stripe, Coinbase, and PayPal each have come out with a solution in the last 30 days addressing this emerging trend.

Provider	Product / Toolkit	Description
Mastercard	Agent Pay	Developed in partnership with Microsoft and IBM. Uses virtual cards, known as Mastercard Agentic Tokens, to enable AI agents to make payments securely and with greater control. Each agent must register and authenticate via a Secure Remote Commerce MCP.
PayPal	MCP Servers & Agent Toolkit	Offers MCP servers and an Agent Toolkit that provide developers with tools and access tokens to integrate PayPal’s payment processes into AI workflows.
Stripe	Agent Toolkit & Issuing APIs	Provides a toolkit for agents and the option to issue single-use virtual cards for agents to use. The Issuing APIs allow for programmatic approval or decline of authorizations, ensuring that purchase intent aligns with authorization. Spending controls are available to set budgets and limit agent spending. Also available as an MCP
Coinbase	AgentKit	Offers a comprehensive toolkit for enabling AI agents to interact with the blockchain. It supports any AI framework and wallet, facilitating seamless integration of crypto wallets and on-chain interactions. With AgentKit, developers can enable fee-free stablecoin payments, enhancing the monetization capabilities of AI agents. The platform encourages community contributions to expand its functionality and adapt to various use cases.
Visa	Visa Intelligent Commerce	Allows developers to create AI agents that can search, recommend, and make payments using special cards linked to the user’s original card. Mark Nelsen, Visa’s global head of consumer products, highlights the transformative impact this system has on shopping and purchasing experiences. Visa Intelligent Commerce

These are just a few of the many tools that anthropic reckognizes (and there’s even more: 5500+, 4500+) ranging from airbnb to oura rings to spotify and everything in between. i’ll be a bit more concrete for what im thinking about later, but picking AI payments as an arbitrary tool example was just meant to segway into whats to come.

AI Firms

Given appropriate tooling, trustworthy authentification and intercommunicated deep intelligence, the fourth level of OpenAI’s AGI roadmap introduces “Innovators” - AI systems capable of developing groundbreaking ideas and solutions across various fields. With the same access to what humans have had for the last N years, these agents will see ways to optimize and discover new cost savings and research discoveries. This stage represents a significant leap forward, as it signifies AI’s ability to drive innovation and progress independently.

To visualize this, conceptualize an agent that is tasked to develop and implement a restaurant menu item. lets say it is a sauce for a pasta dish. with access to a blender, pantry and kitchen utensils, the AI can combinatronically attempt every ingredient x volume x cooking method combination until it arrives at the ‘perfect dish’, innovated from research conducted without (much) human supervision. of coutse, restaurants are from being early adopters, so dont expect this anytime soon. but replace restaurants with wet labs and it is already very commonplace in vaccine research.

There are still a number of bottlenecks here; namely data, energy and feedback collection. This is one of the major upcoming opportunities for the next half-decade.

Ok but now how about collaboration between a group of agents? i should be able to dispatch a gaggle of agents to solve a task and through mutual (chronological) codiscovery they resolve said task - much like we would in a hackathon or school project. This is where things get complicated, not only because of the earlier A2A protocols that need establishing but because there are very few real-life scenerios that have played out like this as reference.

what comes first to mind is an expirement during the cold war where the US government hired three nuclear physicist PhDs and asked them to design a nuke using only public information. these students weren’t einsteins but they were certainly smarter than most ppl. long story short they succeeded. they presented schematics on how to build a working fission bomb. the point: what would happen when you get not 3, or ~20 top-of-their-field experts in a room to discuss and implement projects, but 10,000? 10,000,000,…,000? Even if they dont get any smarter than their current level of top perctentile grad student, the fact that they are digital, that they can be copied infinitely, is a massive unlock. The marginal cost of adding a peak human(+) level intelligence would (currenlty) be a couple dollars an hour.

Source: Google 2.5 Pro release, for reference human expert accuracy percent is 81.3% on GPQA Diamond (MCQ test for PhD knowledge domain experts). Fun fact, non experts score 22.1% which is worse than random guessing

What if Google suddenly had 10 million AI software engineers independently contributing billions of hours of research? Copies of AI Sundar can craft every product’s strategy, review every pull request, answer every customer service message, and handle all negotiations - everything flowing from a single coherent vision. As much of a micromanager as Elon might be, he’s still limited by his single human form. But AI Elon can have copies of himself design the batteries, be the car mechanic at the dealership, and so on. And if Elon isn’t the best person for the job, the person who is can also be replicated, to create the template for a new descendant organization.

This is now where we get into Level 5 AGI, AI “Organizations” that can function as entire entities, possessing strategic thinking, operational efficiency, and adaptability to manage complex systems and achieve organizational goals. While these advanced levels remain theoretical, they represent the ultimate ambition of AI researchers and highlight the potential for AI to revolutionize industries and organizational structures in the future. It is at this point that sufficiently capital funded and competent AIs can decide to ‘make or buy’ a service. Why liscense from A2A services that can do tax filings when the agent can create their own software themselves? This is far away, but where we’re headed, especially if the major payment providers can not support A2A micropayments/tooling when the cost of coding the service from scratch would be less than the smallest macropayment.

and then?

this gets esoteric quickly but we should also think about the second-order effects — the broader changes enabled by new technology. For example, after cars were invented, we built highways, suburbs, and trucking infrastructure. A famous idea in science fiction is that

“good writers predict the car, but great writers predict the traffic jam”-Fredrik Pohl.

Historical data going back thousands of years indicates that population size is the key input for how fast your society comes up with more ideas, and thus progresses. This is a pattern that continues from antiquity into modern day: ancient Athens, Alexandria, Tang dynasty Chang’an, Renaissance Florence, Edo-period Tokyo, Enlightenment London, Silicon Valley VCs and startups, Shenzhen, Bangalore and Tel Aviv. The clustering of talent in cities, universities and top firms (e.g. paypal mafia, traitorous eight) produces such outsized benefits, simply because it enables slightly better knowledge flow between smart people.

but why is it so hard for corporations to keep their “culture” intact and retain their youthful lean efficiency? Corporations certainly undergo selection for kinds of fitness, and do vary a lot by industry and mission. but the problem is that corporations cannot replicate themselves … Corporations are made of people, not interchangeable, easily copied software widgets or strands of DNA .. leading to scleroticism and aging. read more directly from gwern.

AI firms will have population sizes that are orders of magnitude larger than today’s biggest companies - and each AI will be able to perfectly mind meld with every other, resulting in no information lost from the bottom to the top of the org chart. what new ideas will come from this?

media & entertainment

ok i need a dopamine distraction from the spooky future uncertainty.

I dont want to get too deep into the trends of the B2C world but you can basically boil it down to: time and attention are the scarse resources, and that people want entertainment in moments of leisure. The modernization of many services has allowed the average american more leisure time, and this will only increase with more services being productionized by AI Agents and Organizations. Stanford HAI and ‘matter experts’ guestimate 20-30% reduction in working hours by 2030, which essentialy means a 4day work week (finally?). Unlikely, though, as we’ll probably get people to just do 30% more work in the same 5 days. in late stage capitalism, the treadmill speed increases and those who dont adapt will be left behind.

Leisure, one would hope, is used for social gatherings, rest, and creating meaningful life experiences. This was the promise of the early social media networks. Facebook, MySpace and Tumblr had a chronological timeline of events that friends and family would post about online. Much to the chagrin of newspapers, the instantaneous nature of status updates meant that people were increasingly turning to these platforms for news - no need to wait for tomorrow’s newspaper. Facebook caught on and introduced the News Feed in 2006. Initially, the introduction of the news feed was met with backlash from users who were accustomed to a more static and controlled browsing experience. A day later Adam Mosseri (whom i met last week walking with his kids to get boba!) told Casey Newton on Platformer that Instagram would scale back recommended posts, but was clear that the pullback was temporary:

“When you discover something in your feed that you didn’t follow before, there should be a high bar — it should just be great,” Mosseri said. “You should be delighted to see it. And I don’t think that’s happening enough right now. So I think we need to take a step back, in terms of the percentage of feed that are recommendations, get better at ranking and recommendations, and then — if and when we do — we can start to grow again.” (“I’m confident we will,” he added.)

They reimplemented the News Feed soon thereafter because although there was some public outcry (privacy, relevance, etc), the statistics (DAU, engagement, etc) were saying otherwise. Users much prefered this UX, even though the shift to a dynamic feed, where content was constantly curated and updated algorithmically (via EdgeRank), felt intrusive and overwhelming to many. However, over time, as algorithms improved and users adapted, the news feed became an integral part of social media platforms, driving engagement and allowing for more personalized content delivery. This evolution highlights the delicate balance platforms must maintain between innovation and user acceptance, as well as the ongoing challenge of ensuring transparency and trust in algorithmic decision-making.

Michael Mignano calls this recommendation media in an article entitled The End of Social Media:

In recommendation media, content is not distributed to networks of connected people as the primary means of distribution. Instead, the main mechanism for the distribution of content is through opaque, platform-defined algorithms that favor maximum attention and engagement from consumers. The exact type of attention these recommendations seek is always defined by the platform and often tailored specifically to the user who is consuming content. For example, if the platform determines that someone loves movies, that person will likely see a lot of movie related content because that’s what captures that person’s attention best. This means platforms can also decide what consumers won’t see, such as problematic or polarizing content.
It’s ultimately up to the platform to decide what type of content gets recommended, not the social graph of the person producing the content. In contrast to social media, recommendation media is not a competition based on popularity; instead, it is a competition based on the absolute best content. Through this lens, it’s no wonder why Kylie Jenner opposes this change; her more than 360 million followers are simply worth less in a version of media dominated by algorithms and not followers.

Facebook made a nearly existential mistake though. They still relied on the social networks of your friends to decide what you would be interested in. If 10 of your family members liked a post about a cousin’s wedding, their scalable version of collaborative filtering would recommend that same post to you. It was indeed an improvement above what people were being served previously. But TikTok took this a step further. Instead of pulling only from those you are Following, they curated content For You. This could be completely novel videos none of your family have seen or interacted with - just other people that have similar scrolling habits. When you’re connected to 8B people, an infinite feed emerges. instead of the platform having to generate all of the content (TV), users generate all of the content themselves. This detail of personalization was much more precise than previously attainable and marked a profound shift. facebook and people were focused on building social networks, because they think people want to connect what their friends. thats not what they want. they want to sink into their couch, glaze over their eyes and injest nose-puffing humor until (the eternal) sleep. Leisure isnt being used for social fulfilment instead, it is almost entirely comprised of TV (4.28h/day if boomer) or the zoomer equivalent (1.9h/day youtube + 1.5h tiktok).

In a fireside chat with zuck last week, he mentioned where they’re headed next. A more formalized explanation can be found in recent interviews and earning reports.

“AI is not just going to be recommending content, but it is effectively going to be either helping people create more content or just creating it themselves. You can think about our products as there have been two major epochs so far. The first was you had your friends and you basically shared with them and you got content from them and now, we’re in an epoch where we’ve basically layered over this whole zone of creator content. So the stuff from your friends and followers and all the people that you follow hasn’t gone away, but we added on this whole other corpus around all this content that creators have that we are recommending. The third epoch is I think that there’s going to be all this AI-generated content and you’re not going to lose the others, you’re still going to have all the creator content, you’re still going to have some of the friend content. But it’s just going to be this huge explosion in the amount of content that’s available, very personalized and I guess one point, just as a macro point, as we move into this AGI future where productivity dramatically increases, I think what you’re basically going to see is this extrapolation of this 100-year trend where as productivity grows, the average person spends less time working, and more time on entertainment and culture. So I think that these feed type services, like these channels where people are getting their content, are going to become more of what people spend their time on, and the better that AI can both help create and recommend the content, I think that that’s going to be a huge thing. So that’s kind of the second category.”
-Zuck

Sam Lessin (former VP of FB Product) broke this down into five steps in July 2022 (pre-ChatGPT!):

• The Pre-Internet ‘People Magazine’ Era
• Content from ‘your friends’ kills People Magazine
• Kardashians/Professional ‘friends’ kill real friends
• Algorithmic everyone kills Kardashians
• Next is pure-AI content which beats ‘algorithmic everyone’

Zuck is building a 2GW+ datacenter the size of manhattan costing ~$60-65B specifically with aims to improve Llama and leaning into the ai slop. This is them learning from nearly fatal Tiktok risk and Adam Mosseri’s recommendation media: conducted behavior > preferred behavior. Less than 7% (1 in 14) of time on instagram is seeing content from friends, the trend will only continue to decrease.

Source: FTC v. Meta Trial Exhibits

Entertainment itself is evolving. And the trend is towards ever more immersive mediums. Facebook, for example, started with text but exploded with the addition of photos. Instagram started with photos and expanded into video. Gaming was the first to make this progression, and is well into the 3D era. The next step is full immersion — virtual reality — and while the format has yet to penetrate the mainstream this progression in mediums is perhaps the most obvious reason to be bullish about the possibility.

wearables as future

If you’ve seen an advertisement in the last 6 months, chances are you saw one of the new ray ban x meta ambassadors. They are investing an incredible amount of money into this, not only [hundreds of] millions in ad spend, but also eating the cost of production ($1000->$300) for each of the 2M+ sold. Not to mention the $20B in 2025 spend Meta CTO Andrew (Boz) Bozwell’s Reality Labs, topping the $80 billion since 2014, when they purchased VR headset maker Oculus. The Ray Ban glasses are just a stepping stone for their next play - hear it from Boz himself:

AB: So I’ll tell you another demo that we’ve been playing with internally, which is taking the Orion style glasses, actually super sensing glasses, even if they have no display, but they have always-on sensors. People are aware of it, and you go through your day and what’s cool is you can query your day. So you can say, “Hey, today in our design meeting, which color did we pick for the couch?”, and it tells you, and it’s like, “Hey, I was leaving work, I saw a poster on the wall”, it’s like, “Oh yeah, there’s a family barbecue happening this weekend at 4 PM”, your day becomes queryable.

And then it’s not a big leap, we haven’t done this yet, but we can, to make it agentic, where it’s like, “Hey, I see you’re driving home, don’t forget to swing by the store, you said you’re going to pick up creamer”. And it’s like, oh man, there’s all these things that start to change. So you’ve got this VR and MR as this output space for AI, and then you’ve got this kind of AR space where it’s like it’s in the AIs on the inputs.

Fully immersive virtual reality may still be a few years away, but Meta is making a massive bet that Augmented Reality is here, soon. Theyre not the only ones. Google and Warby Parker announced their partnership on May 20th. The next day Jony Ive joins Openai to launch a trailer for a product that will be AI native.

The vision for all of them is the same: Apple needs disrupting. The #1 company in the world by market cap has held a stronghold on the app economy, and interfaces to humans. It is the ultimate consumer product.

Startups and upstarts like limitless, humane pin (now HP), rabbit r1, bee band, and the unfriendly avi behind friend are all jumping at the multi-trillion dollar opportunity as well.

The form factors differ wildly: glasses, pins, necklaces, wristbands, to ‘airpods with a camera’, but they all share the same capacity to have a queryable AI listening/seeing everything. MKBHD’s seminal review of the r1 was a brutal reminder that good enough isn’t good enough in consumer hardware. The final form factor continues to be unclear, but having a new interface to the end users is the bet every one of them are making.

Dont think apple is sleeping on this either, btw, they’re just throwing out flops like Apple Vision Pro and Apple Intelligence for reasons masterfully outlined in bloomberg. Essentially, they never took the AI opportunity seriously:

• Apple’s head of software engineering didn’t believe in AI
• Apple’s head of AI was skeptical about LLMs and chatbots
• Apple’s former CFO refused to commit sufficient funds for GPUs
• Apple’s commitment to privacy limited the company to purchased datasets
• Apple’s AI team is understaffed (and the relative talent level of an AI staffer still at Apple is uncomfortable to speculate about, but, given the opportunities elsewhere, relevant)

This also is taking a massive swing at google, where the interface that every year google pays $20B+ to be the default search engine of may soon dissapate as well. I can just ask my AI wearable to search for information, or perform tasks. Pair this with the recent damaging testimony from Eddy Cue that now that search volume’s down, they may switch: “Oh, maybe we’ll do Perplexity, maybe we’ll do some sort of AI search engine”. There’s more of a possibility of choosing something that is “better” today than there has been any other time. The anti-trust trials are highlighting that Google and Apple would’ve done well to give ground before they’re forced to.

I like to think i was a pioneer back in 2017 with the snapchat spectacles, but theres a lot more we can build now. As the Ray Ban glasses permeate through culture, the gradual acceptance of AR via Pokemon Go (to the polls) and VR Chat reflect an inevitable trend: users are (sometimes) getting off their phone.

economic models

theres a lot more i want to say about some of the inevitabile tech/trends; notably on agentic shopping, logistic moats, web3, robots and digital physicalism but I’m going to save both of us time and abstractify. i’ll write about them later.

ben thompson at stratechery breaks tech into two major philosophies: tech that augments, and tech that automates. he calls these platforms and aggregators - worth a read.

The philosophy that computers are an aid to humans, not their replacement, is the older of the two. The expectation is not that the computer does your work for you, but rather that the computer enables you to do your work better and more efficiently. Apple and Microsoft are its most famous adherents, and with this philosophy, comes a different take on responsibility. Nadella insists that responsibility lies with the tech industry collectively, and all of us who seek to leverage it individually. From his 2018 Microsoft Build Keynote:

We have the responsibility to ensure that these technologies are empowering everyone, these technologies are creating equitable growth by ensuring that every industry is able to grow and create employment. But we also have a responsibility as a tech industry to build trust in technology. This opportunity and responsibility is what grounds us in our mission to empower every person and every organization on the planet to achieve more. We’re focused on building technology so that we can empower others to build more technology. We’ve aligned our mission, the products we build, our business model, so that your success is what leads to our success. There’s got to be complete alignment.

Similarly, Pichai, in the opening of Google’s keynote, acknowledged that “we feel a deep sense of responsibility to get this right”, but inherent in that statement is the centrality of Google generally and the direct culpability of its managers. Facebook has adopted a more extreme version of the same philosophy that guides Google: computers doing things for people. This is the difference in philosophies, more ‘creepily’ put by zuck in a 2018 f8 keynote:

I believe that we need to design technology to help bring people closer together. And I believe that that’s not going to happen on its own. So to do that, part of the solution, just part of it, is that one day more of our technology is going to need to focus on people and our relationships. Now there’s no guarantee that we get this right. This is hard stuff. We will make mistakes and they will have consequences and we will need to fix them. But what I can guarantee is that if we don’t work on this the world isn’t moving in this direction by itself.

Google and Facebook have always been predicated on doing things for the user, just as Microsoft and Apple have been built on enabling users and developers to make things completely unforeseen.

Google and Facebook, are products of the Internet, and the Internet leads not to platforms but to aggregators. While platforms need 3rd parties to make them useful and build their moat through the creation of ecosystems, aggregators attract end users by virtue of their inherent usefulness and, over time, leave suppliers no choice but to follow the aggregators’ dictates if they wish to reach end users. The business model follows from these fundamental differences: a platform provider has no room for ads, because the primary function of a platform is provide a stage for the applications that users actually need to shine. platforms are powerful because they facilitate a relationship between 3rd-party suppliers and end users; Aggregators, on the other hand, intermediate and control it. Google and Facebook deal in the trade of information, and ads are simply another type of information.

Figure: Stratechery

This relationship between the differentiation of the supplier base and the degree of externalization of the network effect forms a map of effective moats; to again take these six companies in order:

• Facebook has completely internalized its network and commoditized its content supplier base, and has no motivation to, for example, share its advertising proceeds.
• Google similarly has internalized its network effects and commoditized its supplier base; however, given that its supply is from 3rd parties, the company does have more of a motivation to sustain those third parties (this helps explain, for example, why Google’s off-site advertising products have always been far superior to Facebook’s).
• Netflix and Amazon’s network effects are partially internalized and partially externalized, and similarly, both have differentiated suppliers that remain very much subordinate to the Amazon and Netflix customer relationship.
• Apple and Microsoft, meanwhile, have the most differentiated suppliers on their platforms, which makes sense given that both depend on largely externalized network effects. “Must-have” apps ultimately accrue to the platform’s benefit.

Another example is Uber: on the one hand, Uber’s suppliers are completely commoditized. This might seem like a good thing! The problem, though, is that Uber’s network effects are completely externalized: drivers come on to the platform to serve riders, which in turn makes the network more attractive to riders. This leaves Uber outside the Moat Map. The result is that Uber’s position is very difficult to defend; it is easier to imagine a successful company that has internalized large parts of its network (by owning its own fleet, for example), or done more to differentiate its suppliers. The company may very well succeed thanks to the power from owning the customer relationship, but it will be a slog. Its already lost in China and APAC, and is rapidly losing market share to Waymo.

On the opposite side of the map are phone carriers in a post-iPhone world: carriers have strong network effects, both in terms of service as well as in the allocation of fixed costs. Their profit potential, though, was severely curtailed by the emergence of the iPhone as a highly differentiated supplier. Suddenly, for the first time, customers chose their carrier on the basis of whether or not their preferred phone worked there; today, every carrier has the iPhone, but the process of reaching that point meant the complete destruction of carrier dreams of value-added services, and a lot more competition on capital-intensive factors like coverage and price.

solutions

i dont know what the solutions are. they will emerge over time. nothing is certain from the onset. Bell, Lee and Zuck couldn’t have fathomed what their innovations would become. I wont pretend to.

But, I want to.

So I’m decided to try by looking at what the history of ads is converging into, what the progress of emerging trends is unlocking, and hopefully adress and find emerging problems that (will) need solutions. to most, especially the incumbents, these solutions will probably look like toys, but don’t be so quick to discard them.

search

with the background pretty established at this point, we can say with confidence that search is being seriously challenged by AI Overviews, and AI (API?) interfaces like chatgpt. from that learning, we calculated earlier that the ~316B in paid search spend (and the ~100B of publisher trickle down economics) is up in the air.

well your first thought may be to try to game the SEO of AI Overviews. Its a good start, and a few others have thought about it.

However, its short sighted and built on shifting sands. Nonetheless, in the rapidly evolving landscape of search, integrating Generative Engine Optimization (GEO) with traditional SEO strategies is crucial for maintaining visibility and brand recognition. so lets break it down and understand exactly what there is to be done in the short term until the dust settles.

Traditional search relies on crawlability, indexability, and rankability. However, AI search introduces retrievability, which determines how effectively AI can access and prioritize your brand’s information. This shift requires SEOs to evolve their strategies to include retrievability, ensuring that core brand information is accessible and prioritized for AI models. Authority, and thus GEO ranking, is built through contextual relevance and consistent brand mentions in other authoritative sources kind of like a modern backlinks system.

Category	Strategy/Metric	Description
Retrievability Optimization	Presence	Ensure consistent brand mentions in contexts that shape AI’s training data.
Retrievability Optimization	Recognition	Build credibility through relevant mentions and associations with trusted entities.
Retrievability Optimization	Accessibility	Structure information for easy retrieval by AI, both on your website and across the web.
On-page SEO	Build Topical Authority	Create entity-rich content and structure it into topic clusters.
On-page SEO	Reinforce Entity Relationships	Use strategic internal and external linking.
On-page SEO	Create Contextually Relevant Content	Align content with evolving user intent and AI-generated responses.
On-page SEO	Establish Thought Leadership	Publish unique insights and research to inspire citations.
On-page SEO	Structure Content for AI Processing	Use clear formats like FAQs and bullet points.
Off-page SEO	Target AI-Trusted Sources	Earn mentions in key industry publications and forums.
Off-page SEO	Use Digital PR	Influence conversations that shape AI’s understanding.
Off-page SEO	Maintain Consistent Brand Representation	Ensure consistent messaging across all channels.
Off-page SEO	Strengthen Knowledge Graph Presence	Optimize Wikipedia and Wikidata entries.
Technical SEO	Keep Data Simple	Use straightforward HTML and clear metadata.
Technical SEO	Use Structured Data	Implement schema markup to enhance entity recognition.
Technical SEO	Prioritize Site Speed	Optimize performance for quick loading.
Technical SEO	Help AI Crawl Content	Allow AI crawlers and fix crawl issues.
AI-Driven Search Success	Impressions and Visibility	Track presence in AI Overviews and featured snippets.
AI-Driven Search Success	Branded Engagement	Monitor branded search volume and direct traffic.
AI-Driven Search Success	Real Business Outcomes	Measure leads, conversions, and revenue.
AI-Driven Search Success	AI Citations and Mentions	Regularly check brand appearances in AI-generated content.

Above are a few ways to improve your ranking aggregated from many of the aforementioned startups and SEO guru websites touting the best way to be the winner source reference in AI Overviews.

It may seem like i’m downplaying this, but recall that gaming the algorithm to be in the AIO nets a jump from 18.7% to 27.4% CTR - which is massive. if you’re ok with being a slave to the algorithm, there is a fantastic opportunity to provide these services – at least until meta and google decide to too.

this is the first of many possible solutions. but this relies on the belief that websites are going to continue creating content, and that users will continue to interface via the internet / google search. Yes, you could make a quick buck in the next 2 years as search is restructured, but you can’t build a (longterm/massivve) business on shifting sands, privy to the whims of Google. need i remind you what happened to yelp?

agentic search

The current form of the web is built to support a user experience with the intent that humans are directly consuming the experiences. Agents working on behalf of humans breaks this expectation and have different needs and requirements for a successful Agent Experience (AX)
Source: Netlify’s Sean Roberts

The web was built for humans, by humans, but these days, we’re not the only ones online. CAPTCHA exists because in the early 2000s Yahoo! was getting hammered by bots (not LLMs) signing up for millions of free email accounts, which were then used to send spam. They enlisted Carnegie Mellon University (yay) computer scientists Luis von Ahn and Manuel Blum to separate humans from bots. Blum was one of those aweseome hippie profs and Luis went on to create duolingo ($DUOL).

Of course, bots didn’t stop there—they moved on to scalping tickets and juicing engagement on posts, which is why robots.txt was so important. now, with LLMs and proper tooling, the line between human and machine on the web is blurrier than ever.

While a human might take several minutes to sift through a webpage, an LLM can parse the same information in milliseconds, extracting relevant data points with remarkable accuracy.

This task was simple enough, as the order and supplier were explicit. But what happens when someone asks their AI Agent to “buy them a red sweater”?

How should google count these impressions? If they stepped away to brew some coffee, would the user even know about any ads on the websites the agent browsed? Should they?

LLMs, unlike humans, do not require visual cues or interactive elements to understand content. Instead, they rely on structured data formats such as JSON or XML, which allow for rapid data extraction and processing. As a result, businesses that adapt their digital strategies to accommodate these preferences are likely to see improved performance metrics, including faster load times and higher data throughput.

data loop

https://www.dwarkesh.com/p/timelines-june-2025

We don’t have a large pretraining corpus of multimodal computer use data. I like this quote from Mechanize’s post on automating software engineering: “For the past decade of scaling, we’ve been spoiled by the enormous amount of internet data that was freely available for us to use. This was enough for cracking natural language processing, but not for getting models to become reliable, competent agents. Imagine trying to train GPT-4 on all the text data available in 1980—the data would be nowhere near enough, even if we had the necessary compute.”

Again, I’m not at the labs. Maybe text only training already gives you a great prior on how different UIs work, and what the relationship between different components is. Maybe RL fine tuning is so sample efficient that you don’t need that much data. But I haven’t seen any public evidence which makes me think that these models have suddenly gotten less data hungry, especially in this domain where they’re substantially less practiced.
The primary audience of your thing (product, service, library, …) is now an LLM, not a human.

But it’s not inevitable that this ends with one gigafirm which consumes the entire economy. As Gwern explains in his essay, any internal planning system needs to be grounded in some kind of outer “loss function” - a ground truth measure of success. In a market economy, this comes from profits and losses.

Internal planning can be much more efficient than market competition in the short run, but it needs to be constrained by some slower but unbiased outer feedback loop. A company that grows too large risks having its internal optimization diverge from market realities.

The market continues to serve as the grounding outer loop. How does the AI firm convert trillions of tokens of data from customers, markets, news, etc every day into future plans, new products, and the like? Does the board make all the decisions politburo-style and use $10 billion dollars of inference to run Monte Carlo tree search on different one-year plans? Or do you run some kind of evolutionary process on different departments, giving them more capital, and compute/labor based on their performance? This is the approach of Eric Reis’s lean startup and most VC portfolio hedging.

LLMs don’t like to navigate, they like to scrape.
LLMs don’t like to see, they like to read.
LLMs don’t like to click, they like to curl.
karpathy https://x.com/karpathy/status/1914494203696177444

daytona, rbg, exa etc
deoosnt even have to be in XML: twitter

Time Period	ChatGPT	DeepSeek	Google	Grok	Perplexity
6 months ago	86.7%	-	6.2%	-	1.9%
3 months ago	79.8%	9.2%	4.9%	-	1.8%
1 month ago	77.6%	7.6%	5.5%	3.2%	1.9%
Today	80.1%	6.5%	5.6%	2.6%	1.5%

Project Mariner
Computer Use
Operator
Exa
pig.dev
You can use Mariner to fill out lengthy forms, write comments, search the web, and more.

Let’s take a closer look at Project Mariner’s key features:

Multi-Modal Understanding: Powered by the latest Google 2025 AI models, Mariner can accurately understand diverse web elements, like forms, images, text, and code. This allows the agent to holistically understand web pages rather than process individual text segments one by one.
Autonomous Navigation: Project Mariner can independently type, scroll, and click on websites. For example, you can ask it to place an online order for a cake on a specific website, and the agent should be able to visit the site, add the cake to your cart, and fill out your information on its own.

This has massive implications for web accessibility, as well. Specially abled users will be able to freely navigate and interact with websites without relying on multiple accessibility tools.
Automation: Mariner excels at automation thanks to multimodal understanding and Google’s 2025 AI models. You can minimize repetitive research tasks and let Mariner take over instead. For instance, consumers can use it to compare the prices and features of different competing products on their screens.
https://coalitiontechnologies.com/blog/google-ai-in-2025-how-search-is-changing

correctness

I suspect that the entire agent approach is going to be disappointing in the real world for the foreseeable future; there are just too many edge cases and unclear decision points that result in products that demo much better than they work.
Ben Thompson, Google I/O: Google’s Strengths and Weaknesses in AI Search

there’s a few reasons for this. the first and biggest is because the LLM revolution is based on non-deterministic outputs. this can cause a lot of enterprise harm if the outputs are not aligned to what you would expect. as such, many companies are building ‘guardrails’ to avoid having the model ‘hallucinate’.

And when i say many, i mean many:

As each of the above companies will tell you, they’ve built a unique and 100x better solution for dealing with enterprise AI security. This can range from RAGs on internal data, to firewall protocols, to a big red button, and everything inbetween. some of these companies have some of the brightest minds building them.

Unfortunately, they too are short sighted and built on the shifting sands of the foundation model companies. Its short sighted because the solution isn’t going to be from band-aiding the outputs of the models in post-editing, but rather, the alignment of the model to its given task during its training - something (at this point) only massive AI research labs can do (and are incentivized to do) due to the size of training jobs. The trends of model correctness are all pointing that way.

ads

can the same be said about ads? will the companies that control the foundational models control the means of production of ads?

there’s only a few places to insert ads with the agentic web:
1. on the display interface that the user will interact with (e.g. NexAD, Bing, Perplexity, etc)
2. in the reasoning
3. in the search (poisoning)
4. in the data: see GEO
5. in the ad
6.

For now it will be interesting to see how Meta’s advertising tools develop: the entire process of both generating and A/B testing copy and images can be done by AI, and no company is better than Meta at making these sort of capabilities available at scale. Keep in mind that Meta’s advertising is primarily about the top of the funnel: the goal is to catch consumers’ eyes for a product or service or app they did not know previously existed; this means that there will be a lot of misses — the vast majority of ads do not convert — but that also means there is a lot of latitude for experimentation and iteration. This seems very well suited to AI: yes, generation may have marginal costs, but those marginal costs are drastically lower than a human.
Jan 2023: https://stratechery.com/2023/ai-and-the-big-five/

but there are so many side effects that go into each of these:
1. user data / history
2. context from search page / product
3. personalization of tone / format
4. intent understanding
5.

WPP has rolled out more than 28,000 AI agents, but scale is only part of the story. The other: how to hardwire control into these systems even before they’re autonomous.

That’s the job of Daniel Hulme, the holdco’s chief AI officer. He’s not focused on flashy demos or one-off tools. He’s trying to engineer the infrastructure to keep tens of thousands of AI agents from drifting out of line — inside the company and beyond its digital walls.

This isn’t theoretical. WPP is already deploying agents to handle media planning, content generation, analytics and optimization. For now, their capabilities are limited to helping human employees without agency to full autonomy to access systems and data sources for safety reasons. But the promise of agentic AI involves coordinating numerous AI systems, orchestrating multiple intelligent systems to connect agents across teams, clients and platforms. Without it, the risk of conflicting behavior, redundancy, or outright failure goes up fast.

In other words, part of WPP’s bet is that the real competitive edge won’t come from using AI so much as it will come from responsibly managing, integrating and scaling it before rivals catch up.

One of Conscium’s projects is an app called “Moral Me,” which crowdsources moral dilemmas from users to help train AI agents on a wider range of human moral values. The logic is simple: as people hand more decision-making to personal AI, marketers and technologists will need to learn how to influence the agents themselves.

Influence the agent, not the audience

It’s not just about personalization, it’s also about building systems that can anticipate intent. That’s the next frontier: agents that know what someone is about to want and marketers who can respond in real-time. Hulme’s team is integrating data from various knowledge graphs and data sources through WPP’s Open, the holding company’s privacy-first campaign planning tool powered by federated learning.
https://digiday.com/media/how-wpp-is-thinking-about-responsibly-scaling-agentic-ai-systems/

Attribution

Attribution has been a cornerstone of success for companies like Google and Facebook, primarily through the use of tracking technologies such as pixels. These pixels allow for detailed tracking of user interactions across the web, providing valuable data that can be used to optimize advertising strategies and improve user engagement. By understanding the journey of a user from ad impression to conversion, these companies can attribute success to specific campaigns and refine their approaches for better results. This data-driven attribution model has enabled them to offer highly targeted advertising solutions, which in turn has driven significant revenue growth and market dominance.

As AI agents become more prevalent in various domains, a significant challenge arises: how to attribute information and actions correctly. Attribution is crucial for accountability, transparency, and trust. Here are some key considerations and potential approaches for attribution in AI agents:

1. Source Attribution:
   - AI agents often aggregate information from multiple sources. It’s essential to track and display the origin of each piece of information. This could be achieved through metadata tagging and maintaining a source log that users can access.

2. Decision Attribution:
   - When AI agents make decisions, understanding the rationale behind those decisions is vital. Implementing explainable AI (XAI) techniques can help in providing insights into the decision-making process, allowing users to see which data points influenced the outcome.

3. User Interaction Attribution:
   - In scenarios where AI agents interact with users, it’s important to attribute actions to either the user or the agent. This can be managed by logging interactions and clearly distinguishing between user-initiated and agent-initiated actions.

4. Content Creation Attribution:
   - For AI-generated content, it’s necessary to indicate that the content was created by an AI. This can be done through disclaimers or watermarks, ensuring that users are aware of the content’s origin.

5. Legal and Ethical Considerations:
   - As AI agents become more autonomous, legal frameworks need to evolve to address attribution issues. This includes defining liability and responsibility for actions taken by AI agents.

6. Technological Solutions:
   - Blockchain technology offers a promising solution for attribution by providing a tamper-proof ledger of actions and data sources. This can enhance transparency and trust in AI systems.

In conclusion, as AI agents continue to evolve, developing robust attribution mechanisms will be essential to ensure accountability and maintain user trust. The future of attribution in AI will likely involve a combination of technological, legal, and ethical strategies to address these challenges.

Here’s the other #thing that is interesting, though, and related: this model actually means that Perplexity isn’t really even competing with Google, at least when it comes to advertisers. There is a sense in digital in which the ad format creates the advertiser; the most famous example is Facebook, which, to take two examples, recovered from the initial COVID slowdown and shook off an advertiser boycott because their advertising base is made up of companies whose existence is predicated on Facebook ads; Google search isn’t maybe quite so extreme, but entire industries are built around search ads, and those advertisers aren’t going to be very interested in this Perplexity product: not only is the monetization model different, but there clearly isn’t any real measurement capabilities in place either.Moreover, starting with brand advertising is normal for a new consumer product; the more important takeaway is that Perplexity is, from an advertising perspective, not really a search engine, but something more akin to a publication or social network.

That, by extension, might mean that the long-term goal isn’t CPC-type search ads, but cost-per-action ads of the type employed by Meta. That will require a lot of work to build out, including extensive data collection, targeting, and measurement capabilities; at the same time, collecting and collating data in order to present something compelling is actually not that far off from what Perplexity’s core consumer offering already is. Or — and this might be the true long-run upside — AI search might end up creating its own advertising base in the long run.
but both options are likely less attractive to advertisers and command less of a premium.
https://stratechery.com/2024/perplexity-search-ads-inventory-defined-advertisers-telegram-follow-up/

form

Use AI to make it so that the ads business goes a lot better. Improve recommendations, make it so that any business that basically wants to achieve some business outcome can just come to us, not have to produce any content, not have to know anything about their customers. Can just say, “Here’s the business outcome that I want, here’s what I’m willing to pay, I’m going to connect you to my bank account, I will pay you for as many business outcomes as you can achieve”. Right? Best black box of all time. Yeah, it is basically like the ultimate business agent, and if you think about the pieces of advertising, there’s content creation, the creative, there’s the targeting, and there’s the measurement and probably the first pieces that we started building were the measurement to basically make it so that we can effectively have a business that’s organized around when we’re delivering results for people instead of just showing them impressions. And then, we start off with basic targeting. Over the last 5 to 10 years, we’ve basically gotten to the point where we effectively discourage businesses from trying to limit the targeting. It used to be that a business would come to us and say like, “Okay, I really want to reach women aged 18 to 24 in this place”, and we’re like, “Okay. Look, you can suggest to us…” sure, But I promise you, we’ll find more people at a cheaper rate. If they really want to limit it, we have that as an option. But basically, we believe at this point that we are just better at finding the people who are going to resonate with your product than you are. And so, there’s that piece.

But there’s still the creative piece, which is basically businesses come to us and they have a sense of what their message is or what their video is or their image, and that’s pretty hard to produce and I think we’re pretty close. And the more they produce, the better. Because then, you can test it, see what works. Well, what if you could just produce an infinite number? Yeah, or we just make it for them. I mean, obviously, it’ll always be the case that they can come with a suggestion or here’s the creative that they want, especially if they really want to dial it in. But in general, we’re going to get to a point where you’re a business, you come to us, you tell us what your objective is, you connect to your bank account, you don’t need any creative, you don’t need any targeting demographic, you don’t need any measurement, except to be able to read the results that we spit out. I think that’s going to be huge, I think it is a redefinition of the category of advertising.

So if you think about what percent of GDP is advertising today, I would expect that that percent will grow. Because today, advertising is sort of constrained to like, “All right, I’m buying a billboard or a commercial…” Right. I think it was always either 1% or 2%, but digital advertising has already increased that. It has grown, but I wouldn’t be surprised if it grew by a very meaningful amount. I’m with you. You’re preaching to the choir, everyone should embrace the black box. Just go there, I’m with you.
https://stratechery.com/2025/an-interview-with-meta-ceo-mark-zuckerberg-about-ai-and-the-evolution-of-social-media/

video of zuck
uring his Sessions appearance, Zuckerberg posited that while creative ad agencies would continue to exist were Meta to deploy this AI, small businesses might not “have to start off with the creative” and Meta could simply handle all of their advertising operations.

In fact, Zuckerberg asserted during Sessions that Meta’s ad tools, several of which have generative AI capabilities, are sophisticated enough already that the company doesn’t even recommend that customers specify the demographics they’d like to target. Meta’s tools can find interested users better than human marketers can, claimed Zuckerberg. The next logical step, he says, is trying to apply this data-driven optimization to the creative side.

“We’re gonna be able to come up with, like, 4,000 different versions of your creative and just test them and figure out which one works best,” said Zuckerberg.
https://techcrunch.com/2025/05/07/mark-zuckerbergs-ai-ad-tool-sounds-like-a-social-media-nightmare/

interface

in classic vincent fashion, we need yet again preface this section by yet another history lesson. but this is getting long and how we learned to communicate is too nuanced to cover in earnest so lets just do a quick speedrun:

communication history

Date	Name	Description
c. 2 million years ago	Primate alarm calls (“grunts”)	Early hominins used instinctive vocalizations to warn of danger, a behavior still observed in modern vervet monkeys.
c. 500 thousand years ago	Emergence of speech capacity (FOXP2 gene)	A mutation in the FOXP2 gene—shared by Neanderthals and modern humans—laid the neural groundwork for complex vocalization.
c. 285 thousand years ago	Pigment use for symbolic engraving	Red ochre pieces engraved with crosshatches at Olorgesailie, Kenya, indicate early symbolic behavior.
c. 100 thousand years ago	Shell-bead personal ornaments	Perforated Nassarius shell beads from Qafzeh Cave, Israel, used for identity and social signaling.
c. 77,000 - c. 40,000 BCE	Symbolism in Early Human Culture	This period marks significant developments in human symbolic expression. Around 77,000 BCE, abstract engravings on ochre at Blombos Cave, South Africa, signified the dawn of visual symbolism. By 64,000 BCE, the first figurative cave paintings, including hand-stencils and animal figures, appeared in Sulawesi, Indonesia, representing the oldest known figurative rock art. Approximately 50,000 BCE, the emergence of oral traditions and myths laid the foundation for spoken storytelling, as seen in the continuous Dreamtime narratives of Australian Aboriginal cultures. By 40,000 BCE, portable “Venus” figurines like the Hohle Fels Venus in Germany conveyed shared cultural symbols in a portable form.
c. 30 000 BCE - c. 5500 BCE	Pictographs and Proto-Writing	This period marks the evolution of early pictorial and symbolic communication. Sophisticated depictions of animals in Chauvet Cave, France, and vast galleries in Lascaux, France, illustrate advanced pictorial communication. In India, Bhimbetka rock-shelter petroglyphs record communal stories in stone. Jiahu proto-symbols in China represent early attempts at proto-writing, while Mesopotamian clay accounting tokens serve as precursors to abstract record-keeping.
c. 3500 BCE - c. 1200 BCE	Early Scripts	This period marks the emergence and development of early writing systems across various civilizations. Proto-cuneiform pictographs on Sumerian tablets from Uruk IV gradually evolved into full writing, leading to the creation of Sumerian cuneiform, the world’s first true writing system, with wedge-shaped impressions on clay tablets in Uruk, Iraq. Simultaneously, Egyptian hieroglyphs emerged as a complex pictographic-ideographic script on early dynastic monuments along the Nile. In South Asia, the Indus Valley script featured undeciphered symbols on seals from Harappa and Mohenjo-Daro, indicating urban communication. In China, the oracle-bone script appeared with inscribed divinatory characters on ox scapulae during the Shang dynasty, representing the earliest form of Chinese writing.
c. 1050-600 BCE	Alphabet	The evolution of the alphabet began with the Phoenician alphabet, a streamlined consonant-only script from the Levant, which served as the ancestor to most later alphabets. This was followed by the Greek alphabet around 800 BCE, which adopted Phoenician signs and introduced distinct vowels, enabling full phonetic representation. By 600 BCE, the Aramaic script had spread as the lingua-franca of empires, with Aramaic letters simplifying and uniting diverse peoples in writing.
c. 500 BCE - c. 100 BCE	Developments in Grammar	This period saw significant advancements in linguistic codification: Pāṇini’s Aṣṭādhyāyī systematically codified Sanskrit’s phonetics and morphology, marking the earliest linguistic treatise; the Qin dynasty standardized the Chinese script into the small seal script to unify the first Chinese empire; and Dionysius Thrax’s “Art of Grammar” emerged as the first surviving Western grammar, fully codifying the rules of written Greek.

thanks chatgpt. headpats shoggoth

anyway, this is important because i want to get into the under utilized and perhaps opportunities for the future of search and agent interfacing. this is where things start to get esoteric, but bear with me.

language

ah, written language, the foundation of human civilization. we use it every day. look at me, im doing it rn!

written information, as we know, helped us distribute ideas asynchronously and at scale. that information can be ads too. but it can only convey so much information, and only so fast.

Metric	Description	Q1 (25th pct)	Median (50th pct)	Q3 (75th pct)
Typing speed (computer keyboard)	Average speed for general users, reflecting typical computer usage.	\~35 WPM	43 WPM	\~51 WPM
Typing speed (professional typists)	Speed range for professionals, indicating high proficiency and efficiency.	\~43-80 WPM	80-95 WPM	\~120+ WPM
Stenotype typing speed	Speed using stenotype machines, common in court reporting for rapid input.	\~100-120 WPM	360 WPM	\~360 WPM
Handwriting speed (adult)	Typical speed for adults writing by hand, slower than typing.	\~5-13 WPM	13 WPM	\~20 WPM
Handwriting speed (shorthand)	Speed using shorthand, a method for fast writing by using symbols.	\~350 WPM	350 WPM	\~350 WPM
Morse code speed (manual)	Speed of manual Morse code, used in telecommunication for encoding text.	\~20 WPM	20 WPM	\~70 WPM
Morse code speed (typewriter)	Speed using a typewriter for Morse code, faster than manual transmission.	\~75.6 WPM	75.6 WPM	\~75.6 WPM
Silent reading (non-fiction)	Speed of reading non-fiction silently, reflecting comprehension pace.	\~206 WPM	238 WPM	\~269 WPM
Silent reading (fiction)	Speed of reading fiction silently, often faster due to narrative flow.	\~230 WPM	260 WPM	\~290 WPM
Subvocalization	Slowest reading form, involving internal vocalization of each word.	\~213 WPM	225 WPM	\~238 WPM
Auditory reading	Faster than subvocalization, involves hearing words silently.	\~413 WPM	425 WPM	\~438 WPM
Visual reading	Fastest reading form, recognizing words as visual units without speech.	\~513 WPM	575 WPM	\~638 WPM
Reading aloud (17 languages)	Speed of reading aloud across multiple languages, showing verbal fluency.	\~155-213 WPM	184 WPM	\~213-257 WPM
Audiobook narration speed	Standard speed for narrating audiobooks, balancing clarity and engagement.	\~150-160 WPM	150-160 WPM	\~150-160 WPM
Slide presentation speed	Speed of delivering presentations, ensuring audience comprehension.	\~100-125 WPM	100-125 WPM	\~100-125 WPM
Auctioneer speaking speed	Speed of auctioneers, characterized by rapid speech for bidding processes.	\~250 WPM	250 WPM	\~250 WPM
Fastest speaking (record)	Record speed for speaking, showcasing extreme verbal agility.	\~586-637 WPM	637 WPM	\~637 WPM

Sources: WordsRated, Wikipedia, Reading Rate Meta-Analysis

You’d probably agree that theres obviously gonna be a lot of variance in the Words Per Minute (WPM) for each language. Some languages are indeed very verbose, and some can contain a lot of nuance in just a few words. My goal in this table wasnt to argue that, but to show how much information we can convey/understand within a minute. even world record holders are only producing 650 WPM, and reading (skimming) 2000 WPM (with ~50% reading comprehension).

How much information does that convey? Can we measure this? will it vary across languages?

Figure: Language transmission speed comparison.

Research shows that despite the differences in languages, they all convey information at about 39 bits per second. This means human languages are actually very similarly efficient at sharing information, no matter how they sound or are structured. Across 17 different languages the researchers found that languages inherently balance how much information is in each syllable with how fast we speak to keep communication effective. They suggest that languages may have evolved to fit our brain and body capabilities. But is that the limit?

i ask this bc one of the philosophers on my personal mount rushmore, Wittgenstein, argued that indeed the limits of our language are the limits of our world. What if we went beyond just written language?

voice

Zuck on stage at LlamaCon: I do think voice is under-indexed today. Today 95% of interaction is text [typing] but … I think voice will be a lot bigger going forward.

It was a year prior to the aforementioned iOS 6 that Apple first introduced the idea of an assistant in the guise of Siri; for the first time you could (theoretically) compute by voice. It didn’t work very well at first (arguably it still doesn’t), but the implications for computing generally and Google specifically were profound: voice interaction both expanded where computing could be done, from situations in which you could devote your eyes and hands to your device to effectively everywhere, even as it constrained what you could do.
Ben Thompson, Google and the Limits of Strategy (2016)

That prev table above highlighted reading speeds as well as speaking speads. Reading aloud in 17 languages averages between 155-213 WPM, which we can use as a rough benchmark for normal speech communication rate. Audiobook narration has been studied for balancing clarity and engagement, and suggests maintaining a standard speed of 150-160 WPM. Slide presentations are delivered at 100-125 WPM to ensure audience comprehension.

Note how thats 4-5x more than the ~43 WPM we type on the computer. Its no wonder that adoption for voice tech has taken off recently. Its just easier to communicate by voice. We evolved that way, trading myths and folklore orally for 10s of thousands of years.

Since 2019, voice input has been the second-most popular method of input, with 48% of web searchers already having used voice to conduct searches. It is just behind the mobile web browser. 85% of smartphone owners use voice commands while 39% prefer intelligent speakers. Google voice search statistics confirm that 36% of users use Apple Siri or Google Assistant. Amazon’s Alexa is second, with 25%. Microsoft’s Cortana is third with 19%. (Source: Search Engine Land)

While true that 50-60 % of my AI usage these days is through voice I found that most peolle need a lot of experience to formulate prompts via voice.

Writing gives you more time to think because it’s both slower and makes editing easier than dictating.

Voice has potential but not in a chat interface imo.

Top 5 Voice Search Statistics for 2023
- As of 2023, there are 4.2 billion active voice assistant devices in circulation.
- 27% of searches in the Google App are now done by voice.
- 1 billion voice searches occur each month via mobile and voice search devices.
- Voice search drives more than $2 billion in sales and making voice search optimization part of your strategy will help you sell more and boost revenue.
- Voice search is projected to continue growing in popularity and usage. Search by voice is expected to drive over $40 billion in sales by 2023.

Top 5 Voice Search Statistics Prediction for 2024 & 2025
- In 2024, the number of active voice assistant devices worldwide will double, reaching a total of 8.4 billion units.
- Around 80% of voice searches are predicted to be conversational by 2024, diverging from traditional search methods.
- Specific keyword usage (like “best,” “easy,” “free,” “top,” “list,” etc.) in voice searches is predicted to increase by 20%.
- Expect a major surge in “near me” and local searches, which make up 76% of voice searches, set to triple as users look for local business info directly.
- The global voice recognition market is projected to reach $26.8 billion by 2025.

Top 10 Voice Search Statistics For Local Business
- More than 58% of users use voice search to find local businesses.
- 82% of smartphone users use a search engine when looking for a local business.
- 88% of consumers who conduct a local search on their smartphone visit or call a store within a day.
- Local mobile searches are growing 50% faster than overall mobile searches.
- 46% of all Google searches are seeking local information.
- 18% of local smartphone searches led to a purchase within a day.
- 78% of location-based mobile searches result in an offline purchase.
- 86% of people look up the location of a business on Google Maps.
- 27% of users visit a local business’s website after conducting a voice search.
- 25% of consumers express their willingness to try local voice search despite not having done so yet.

Top 13 Voice Search Statistics For SEO (Search Engine Optimization)
- 50% of voice search results rely on a featured snippet to provide users with information.
- Voice searches are 3 times more likely to be used for local search queries compared to text searches.
- 58% of consumers have used voice search to find local business information in the last 12 months.
- By 2020, 50% of all searches are expected to be voice searches.
- 40% of adults use voice search daily.
- 72% of voice-activated speaker owners use voice search to find information on local businesses.
- Voice search queries are typically longer, with an average length of 29 words.
- 22% of voice search queries are for local content.
- 76% of smart speaker users conduct local searches at least weekly.
- Search queries with “near me” or “close by” have surged in recent years, with Google noting a remarkable 900% increase over a two-year period.
- Approximately 33% of clicks go to the local “snack pack” results, with the remaining 67% going to organic results, based on various SEO studies. The local pack features relevant local business listings at the top of Google search results.
- Google reports that about 88% of consumers who perform a local smartphone search visit or contact a store within a day, highlighting the influential role of mobile search in driving offline conversions.
- Around 22% of voice search queries focus on location-based content, offering multi-location businesses an opportunity to drive foot traffic to their various branches.

Top 6 Voice Search Statistics For Reputation Management
- Reviews are pivotal in local search, with nearly 90% of consumers relying on them to evaluate local businesses. Excellent reviews can lead to a 31% increase in consumer spending.
- Voice searches influence brand reputation, as 52% of consumers use voice-activated speakers to research products or services.
- 27% of voice search users visit the website of a local business after making a voice search, underscoring the importance of positive online reviews for reputation management.
- Voice search users are particularly inclined to engage with local businesses, with 76% of smart speaker users making weekly voice searches for local information.
- Approximately 22% of voice search queries are looking for location-based content, indicating the relevance of optimizing for local search and reputation management.
- Maintaining a positive online reputation is crucial, as 60% of voice search users feel that voice assistants provide more accurate information than traditional search.

Top 5 Voice Search Statistics For Automotive Industry
- Comscore predicts that 50% of all searches will be voice searches by 2020, many of which will be related to automotive information.
- 62% of car owners who use voice assistants in their vehicles have used them to find nearby businesses, including auto services.
- 41% of voice assistant users have asked for directions to a local dealership or service center.
- 44% of consumers have used voice search to inquire about car prices.
- Voice searches for “car dealerships near me” have increased by 200% over the past two years.

Top 5 Voice Search Statistics For Healthcare
- 68% of healthcare providers believe that voice search will become important for patient engagement.
- 21% of voice assistant users have asked for information about healthcare providers or medical services.
- 32% of patients have used voice search to find healthcare providers.
- 42% of healthcare providers have adopted or plan to adopt voice assistant technology for patient engagement and support.
- Voice searches for “doctor’s office near me” have grown by 50% year over year.

Top 5 Voice Search Statistics For Retail
- 58% of consumers have used voice search to find local business information in the last 12 months.
- 24% of consumers have used voice assistants for online shopping.
- 40% of voice assistant users have used them to search for product information.
- 71% of consumers say they would prefer to use voice search to find out the price of products.
- 29% of shoppers have used voice commerce to make a purchase.

Top 5 Voice Search Statistics For Restaurants
- 55% of teenagers and 41% of adults use voice search to discover restaurants.
- 50% of diners have used voice search to find information about a restaurant within the past month.
- 68% of consumers have used voice search to find restaurant hours and directions.
- 61% of diners have used voice search to make restaurant reservations.
- Voice searches for “restaurants near me” have grown 130% year over year.

Top 5 Voice Search Statistics For E-commerce
- Voice-based commerce will reach $80 billion annually by 2023.
- 26% of voice assistant users have made a purchase using voice search.
- 43% of consumers use voice search to research products.
- 40% of millennials have used voice assistants to make a purchase.
- 20% of consumers have made a voice-activated speaker purchase using voice commerce.

Top 5 Voice Search Statistics For Hospitality
- 46% of travelers have used voice search to research or book accommodations.
- 57% of hotel guests who have used voice-activated devices during their stays have done so to request information about the hotel or local attractions.
- 61% of hotel guests have used voice-activated devices to adjust room temperature, lighting, or other in-room controls.
- Voice searches for “hotels near me” have increased by 500% over the past two years.
- 72% of travelers say they would prefer to use voice-activated assistants to request hotel services, such as room service or housekeeping.

Top 5 Voice Search Statistics For Banking
- 35% of banking customers use voice assistants for inquiries or banking transactions.
- 57% of voice assistant users have used them to check their bank account balances.
- 32% of consumers have used voice assistants to pay a bill or transfer money.
- 69% of consumers say they would be comfortable using voice authentication for banking transactions.
- 28% of consumers have asked their voice assistant for information about financial services or banking.

Top 5 Voice Search Statistics For Bitcoin and Cryptocurrency
- 13% of Americans have used voice-activated assistants to inquire about cryptocurrency prices or news.
- 21% of crypto investors have used voice search to check the prices of cryptocurrencies.
- Voice searches for “Bitcoin price” have increased significantly during periods of cryptocurrency market volatility.
- A popular cryptocurrency exchange reported a 28% increase in voice searches related to cryptocurrencies in one year.
- 27% of cryptocurrency enthusiasts use voice-activated devices to stay updated on the latest news and trends in the crypto market.

Top 5 Voice Search Statistics For General Industries
- Home Improvement: 72% of voice-activated speaker owners use them to find information on local businesses.
- Technology: By 2022, it’s projected that 55% of U.S. households will own a smart speaker.
- Legal Services: Voice search often pulls information from featured snippets, which local law firms can optimize for to improve their visibility in voice search results.
- Entertainment and Events: 39% of voice assistant users look for event information.
- Auto Repair: 28% of voice searches are related to location-based queries.

Why haven’t brands fully exploited multimodal AI for shoppable video and voice shopping? Despite AR/VR growth, voice-first commerce remains <1 % of e-com spend—an untapped frontier for “ask and buy” agentic experiences in headsets and smart speakers.
Voice search is also changing how people shop, with 13.6% of the U.S. population (38.8 million people) using smart speakers to make purchases.
“Near me” and local searches make up 76% of voice searches and are expected to grow as more people use voice search to find local businesses.

Sector	Voice Search Users In US
Weather	75%
Music	71%
News	64%
Entertainment	62%
Retail	54%
Food delivery and restaurants	52%
Information about sales, deals and promotion	52%
Healthcare and wellness	51%
Consumer packaged food	49%
Local services	49%
Personalized tips and information	48%
Making a reservation	47%
Fitness	46%
Fashion	45%
Travel	43%
Information about upcoming events or activities	42%
Finance	42%
Other	42%
Options to find information about store location and hours	39%
Access to customer support or service	38%

Source: BrightLocal, Think with Google, Oberlo

image

The information bandwidth of human vision is over 1 million times higher than the bandwidth of reading or listening to language. Product designers take advantage of this with carefully crafted visual interfaces that efficiently convey complex information and help users take action on it.

Say you want to compare restaurant options. Most likely, a scrollable map with location pins, overlays with photos or reviews, and buttons for common filters will be more effective than typing all your criteria into a chat interface and reading the results one at a time.

google search with lens increasing usage

Figure: AI Shopping market map.

[CEO Mark Zuckerberg] has mentioned it on calls, multiple times basically every call.

We certainly want to make sure that video on Facebook is healthy, we think video is going to be increasingly how people communicate and consume information

thought

that the brain is a bottleneck.
In addition, people know that listening to 120% speed sound does not hinder understanding, so the bottleneck is not the “listening to voice” part, but the stage of thinking together It has been.
If we revisit the table from earlier, assuming 1 word ≃ 6 bytes (5 letters + space) in ASCII (1 byte/char → 48 bits/word), we have:

$\text{bits/s} = \frac{\text{WPM} \times 48}{60} = 0.8 \times \text{WPM}$

Metric	Q1 bits/s	Median bits/s	Q3 bits/s
Typing speed (computer keyboard)	28	34.4	40.8
Typing speed (professional typists)	49.2	70	96
Stenotype typing speed	88	288	288
Handwriting speed (adult)	7.2	10.4	16
Handwriting speed (shorthand)	280	280	280
Morse code speed (manual)	16	16	56
Morse code speed (typewriter)	60.5	60.5	60.5
Silent reading (non-fiction)	164.8	190.4	215.2
Silent reading (fiction)	184	208	232
Subvocalization	170.4	180	190.4
Auditory reading	330.4	340	350.4
Visual reading	410.4	460	510.4
Reading aloud (17 langs)	147.2	147.2	188
Audiobook narration speed	120–128	124	128
Slide presentation speed	80	90	100
Auctioneer speaking speed	200	200	200
Fastest recorded speaking	489.2	509.6	509.6

39.15 bits isnt a lot of information encoding, especially by modern file exchange standards. Recall that in 2005, internet bandwidth became capable of data transfers at ~2 megabits per second (2,000,000 bits), which enable Youtubed to stream video. this is a 51082× increase in raw bit-rate capacity. nowadays, avg broadband connections yield ≈95.1 Mbps, so multiply by another 100x. But if ur a pro gamer, or have the purchasing power to pay a whopping $50/month, you can get 1Gbps.

But that has the constraint of network packetswitching. Machine-to-machine (M2M) links at data centers like Elon’s Colossus Supercluster today use 400 GbE, with 800 GbE rolling out. by 2030, 1.6 TbE (1.6 Tbps) is expected to predominate for server-to-server traffic. How fast will broadband traffic get to? How fast does it need to get to?

Model	Tokens/sec	Tokens/min	MB/min
GPT-3	~11,300	~678,000	~21.7
Llama 2 7B	1,200	72,000	~2.3
DeepSeek R1	100	6,000	~0.2
Llama 4 8B	~2,500	~150,000	~4.8
GPT-4o	~5,000	~300,000	~9.6
DeepSeek V2	~300	~18,000	~0.6

intent translation

one of my goals in finding a solution is to find an evergreen problem.

there will always exist a problem of communicating the thoughts in my head. we tried to solve this by codifying a set of words that symbolize meaning, but it has limited us to 39.15bps. how can we go past this limit?

an image is worth a thousand words. how many images can we see in a minute? how many words would that equate to?

neuralink is an extrapolation of this line of thought (pun not intended). It’s probably not something that we can build for in 2025, but it’s certainly a moonshot worth pursuing. Our fleshy brains are probably incapable of the token/information throughput of LLM models, so they’ll always be capable of more ‘intelligence’.

without getting too lost in the future’s uncertainty, what we can be certain about is that current AI agents are being (increasingly) implemented in enterprise use cases. there has been a lot of work done on the GEO for AIO, indexability/retrievability of information, and risk mitigation of the AI outputs. What has been lacking, though, is the advent of better ways to communicate my expected output from the model.

this requires clarity from the human inputter. its just a hunch, but something worth solving for.

llama prompt optimization:
https://github.com/meta-llama/llama-prompt-ops

socratic dialogue
https://x.com/dwarkesh_sp/status/1927769721081827544

Models will communicate directly through latent representations, similar to how the hundreds of different layers in a neural network like GPT-4 already interact.3 So, approximately no miscommunication, ever again. The relationship between mega-Sundar and its specialized copies will mirror what we’re already seeing with techniques like speculative decoding – where a smaller model makes initial predictions that a larger model verifies and refines.

Unlike humans, these models can amalgamate their learnings across all their copies. So one AI is basically learning how to do every single job in the world. An AI that is capable of online learning might functionally become a superintelligence quite rapidly without any further algorithmic progrss. Future AI firms will accelerate this cultural evolution through two key advantages: massive population size and perfect knowledge transfer. With millions of AGIs, automated firms get so many more opportunities to produce innovations and improvements, whether from lucky mistakes, deliberate experiments, de-novo inventions, or some combination.

AI firms will look from the outside like a unified intelligence that can instantly propagate ideas across the organization, preserving their full fidelity and context. Every bit of tacit knowledge from millions of copies gets perfectly preserved, shared, and given due consideration.

Merging will be a step change in how organizations can accumulate and apply knowledge. Humanity’s great advantage has been social learning – our ability to pass knowledge across generations and build upon it. But human social learning has a terrible handicap: biological brains don’t allow information to be copy-pasted. So you need to spend years (and in many cases decades) teaching people what they need to know in order to do their job. Look at how top achievers in field after field are getting older and older, maybe because it takes longer to reach the frontier of accumulated knowledge.

back-to-reality

everythign on all the time - legal implications?
the world post phone can be good?

Can’t wait for a world where Apple Watch is on my wrist; AirPods in my ears; iPhone in my pocket; Apple Glasses on my face; and this product wrapped around my neck.

project astra

https://www.mariehaynes.com/more-ai-innovations-coming-to-search-googles-q4-2024-earnings-call/
https://blog.google/products/android/android-xr/
https://coalitiontechnologies.com/blog/google-ai-in-2025-how-search-is-changing

detour
https://x.com/pitdesi/status/1917557362224947647

live ai
https://www.meta.com/blog/ray-ban-meta-v11-software-update-live-ai-translation-shazam/?srsltid=AfmBOop2dudvJx5CkkZJvu2YGbJYG5EYKBW_pfbJt3_lNDf1qezK-IV5

https://www.meta.com/help/ai-glasses/955732293123641/?srsltid=AfmBOoqvlPXNy2EgqoGSoLzmO9ZX9gis5_VuJboyF_7RQ8SpzIvlwRCy
https://www.theverge.com/2025/1/26/24351264/live-ai-ray-ban-meta-smart-glasses-wearables

Memory

You can now use supermemory MCP in claude dot ai!!!
https://x.com/DhravyaShah/status/1918046775694541039
https://www.newinternet.tech/p/the-new-moat-memory
https://blog.plasticlabs.ai/blog/Launching-Honcho;-The-Personal-Identity-Platform-for-AI

Semil Shah: Do you see any similarities from your time at Facebook with Facebook platform and connect, and how Uber may supercharge their platform?
Chamath Palihapitiya: Neither of them are platforms. They’re both kind of like these comical endeavors that do you as an Nth priority. I was in charge of Facebook Platform. We trumpeted it out like it was some hot shit big deal. And I remember when we raised money from Bill Gates, 3 or 4 months after — like our funding history was $5M, $83 M, $500M, and then $15B. When that 15B happened a few months after Facebook Platform and Gates said something along the lines of, “That’s a crock of shit. This isn’t a platform. A platform is when the economic value of everybody that uses it, exceeds the value of the company that creates it. Then it’s a platform.”
http://haystack.vc/2015/09/17/transcript-chamath-at-strictlyvcs-insider-series/
https://stratechery.com/2020/apple-and-facebook/

Conclusion

Chris Dixon: Over the last 20 years, an economic covenant has emerged between platforms—specifically social networks and search engines—and the people creating websites that those platforms link to. If you're a travel website, a recipe site, or an artist with illustrations, there's an implicit covenant with Google. You allow Google to crawl your content, index it, and show snippets in search results in exchange for traffic. That's how the internet evolved.

David George: And it's mutually beneficial.

Chris Dixon: Mutually beneficial. Occasionally, that covenant has been breached. Google has done something called one-boxing, where they take content and display it directly. I was on the board of Stack Overflow, and they did this—showing answers directly in search results instead of driving traffic to the site. They've done it with Wikipedia, lyrics sites, Yelp, and travel sites.

David George: Yeah, they did it with Yelp.

Chris Dixon: And people get upset. With Yelp, they promoted their own content over others. These issues existed, but the system still worked. Now, in an AI-driven world, if chatbots can generate an illustration or a recipe directly, that may be a better user experience. I'm not against it—it's probably better for users. But it breaks the covenant. These systems were trained on data that was put on the internet under the prior covenant.

David George: Under the premise that creators would get traffic.

Chris Dixon: That's right.

David George: And they could monetize it.

Chris Dixon: Exactly. That was the premise and the promise. Now, we have a new system that likely won't send traffic. If these AI models provide answers directly, why would users click through? We're headed toward a world with three to five major AI systems where users go to their websites for answers, bypassing the billion other websites that previously received traffic. The question is, what happens to those sites?

This is something I've been thinking about, and I'm surprised more people aren't discussing it. I feel like I'm screaming into the abyss.
https://a16z.com/ai-crypto-internet-chris-dixon 

You’re not alone in the abyss, chris. the abyss stares back.

trash

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Newspapers As The New York Times emphasizes “journalism so strong that millions will pay for it,”
PMM: optimize not for what people need, but what they need to hear

role of ceo is to make sure leaders talk to each other

you can tell if brand dont think from customer POV if their website is an org chart/pitch deck.

7 fundamental competitive adv - stephen powers
lean into these to determine your markeitgn strategy

find them reporting to CMO in series B/C.

cramer@mkt1.co
June 2002
Joel Spolsky’s Strategy Letter V, particularly this famous line:
https://www.joelonsoftware.com/2002/06/12/strategy-letter-v/
Smart companies try to commoditize their products’ complements.

Scroll subscribers were never served ads in the first place. Publishers would instead get paid their usage-based share of the Scroll user’s subscription fee, which Scroll claimed was more than a publication would have made by serving that user ads.
may 2021 https://stratechery.com/2021/market-making-on-the-internet/

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One of the great paradoxes for newspapers today is that their financial prospects are inversely correlated to their addressable market. Even as advertising revenues have fallen off a cliff – adjusted for inflation, ad revenues are at the same level as the 1950s – newspapers are able to reach audiences not just in their hometowns but literally all over the world.

2014 https://stratechery.com/2014/economic-power-age-abundance/

Oct 2016

feb 2016 https://stratechery.com/2016/the-reality-of-missing-out/
https://stratechery.com/2020/apple-and-facebook/

the 60 Minutes report explained why in the voiceover:

1. Cater to Google.
   Or you could cater to Facebook, which meant a heavy emphasis on click-bait and human interest stories that had the potential of going viral
   Both approaches, though, favored media entities with the best cost structures, not the best content, a particularly difficult road to travel given the massive amounts of content on the Internet created for free.

The way to deal with both challenges is the same way you break through the noise: you put more focus on fewer brands. Focus, from the perspective of brands, meant advertisements for specific products (colloqially: hero products).
https://stratechery.com/2020/never-ending-niches/

so much of the current ad model relies on discovery. ppl end up clicking on 10 different links before finding what you need. in a world where answers come first, those multiple visits vanish in favor of a single, precise answer/result. if Google’s standard desktop SERP has four paid slots above the fold (Ad Positions 1–4)

it shouldn’t be a surprise that the solutions to discovery and distribution developed differently.
What matters now is dominating search. That is the primary way people arrive at product pages like this.
https://stratechery.com/2016/tv-advertisings-surprising-strength-and-inevitable-fall/

Facebook

The point about effectively infinite competition, though, is a critical one. Neither reach nor timeliness were differentiators, but rather commodities; the companies that dominated on the Internet were those — Google and Facebook in particular — that made sense of the abundance that resulted.

At a high level the concept is that your browser keeps track of topics you are interested in; sites can access that list of topics to show relevant ads. From the Topics API overview:

The diagram below shows a simplified example to demonstrate how the Topics API might help an ad tech platform select an appropriate ad. The example assumes that the user’s browser already has a model to map website hostnames to topics.

Figure: how advertisers would use the topics api

A design goal of the Topics API is to enable interest-based advertising without sharing information with more entities than is currently possible with third-party cookies. The Topics API is designed so topics can only be returned for API callers that have already observed them, within a limited timeframe. An API caller is said to have observed a topic for a user if it has called the document.browsingTopics() method in code included on a site that the Topics API has mapped to that topic.

Imagine if Google had an entire collection of system prompts that mapped onto the Topics API (transparently posted, of course): the best prompt for the user would be selected based on what the user has already showed an interest in (along with other factors like where they are located, preferences, etc.). This would transform the AI from being a sole source of truth dictating supply to the user, to one that gives the user what they want — which is exactly how Aggregators achieve market power in the first place.

This solution would not be “perfect”, in that it would have the same problems that we have today: some number of people would have the “wrong” beliefs or preferences, and personalized AI may do an even better job of giving them what they want to see than today’s algorithms do. That, though, is the human condition, where the pursuit of “perfection” inevitably ends in ruin; more prosaically, these are companies that not only seek to serve the entire world, but have cost structures predicated on doing exactly that.

That, by extension, means it remains imperative for Google and the other Aggregators to move on from employees who see them as political projects, not product companies. AIs have little minds in a big world, and the only possible answer is to let every user get their own word. The political era of the Internet may not be inevitable — at least in terms of Aggregators and their business models — but only if Google et al will go back to putting good products and Aggregator economics first, and leave the politics for us humans.

Assistants are only useful if they are available, which in the case of hundreds of millions of iOS users means downloading and using a separate app (or building the sort of experience that, like Facebook, where users will willingly spend extensive amounts of time in)
Oct 2016 https://stratechery.com/2016/google-and-the-limits-of-strategy/

With regards to multi-step reasoning, this characterization of an “agent” is much more compelling than the one above, because it is dealing with uncertainty, as opposed to trying to complete some discrete set of tasks. Second, the way in which multi-step reasoning results are displayed are quite conducive to advertising — which, by the way, can be that much more targeted the more fleshed out a query is. In other words, to the extent Google can train its users to leverage multi-step reasoning the better it could turn out to be for their business.

It’s a similar story with the planning capability: it should be pretty straightforward to insert ads in a way that makes sense, and there are obvious opportunities for building out a fully-contained shopping experience, with all of the targeting and attribution possibilities entailed in that.

Finally, what was striking to me about the “inspiration” SERP was the extent to which it feels more like a feed than a results page. Again, this makes sense: if you’re looking for inspiration you’re in more of an exploratory mode than a research one, and this new format could be more compelling. And, once again, it should be pretty amenable to advertising, particularly units that are more focused on the top of the funnel as opposed to bottom-of-the-funnel search ads.
https://stratechery.com/2024/google-i-o-googles-strengths-and-weaknesses-ai-search/

robots

physical world

shopping

amazon rufus
google shopping
chatgpt shopping

web3

2005 O’Reilly Web 2.0…refers to websites that emphasize user-generated content, ease of use, participatory culture and interoperability (i.e., compatible with other products, systems, and devices) for end users.
https://www.oreilly.com/pub/a/web2/archive/what-is-web-20.html

This is the part that Web 2.0 got wrong; much like the Facebook model of social networking emphasized being your whole self, Web 2.0 assumed that your one identity would connect together the different pieces of your web existence. However, just as the future of social networking is about different identities for different contexts, interoperability via markets is about linking together distinct user bases in a way that is appropriate for different services, all under the control of the user who is paying for the privilege.
the commonality between what Twitter appears to be building, the phenomenon that Spotify is seeking to plug into, and Shopify and e-commerce is the inherent friction of transferring money (usually via Stripe), for something that is not flattened, but differentiated.
may 2021 https://stratechery.com/2021/market-making-on-the-internet/

agentic economy tracking
https://catenalabs.com/

Since labor is trivial to copy and spin up, the value of intellectual property will go up. The essence of the firm basically becomes intellectual property. GM can poach as many Tesla engineers as it wants (or, in our hypothetical, clone someone with equivalent skills). But without intellectual theft, they can’t get the FSD model or the millions of hours of driving it was trained on. If firms no longer have a moat in labor, their moat will be this kind of industry-specific knowledge and data.

…

Apple’s iOS 14.5 update, released in April 2021, included an App Tracking Transparency (ATT) feature that has had a notable impact on digital advertising

CFO David Wehner said on the call. “We believe the impact of iOS overall as a headwind on our business in 2022 is on the order of $10 billion. So it’s a pretty significant headwind for our business.”

---

everyone looked at e-commerce as, “Wow, look, you could keep all the margin”. You don’t have to pay retailers, you don’t have to do all these sorts of things, and you still have to acquire customers. I thought you had a really good bit in this note that rent was a fixed cost and people would walk up to your store, but what you have to pay on Facebook or Google to acquire that customer, that’s variable. Oh, by the way, thanks to ATT and other things, they can extract ever more of your margin and you don’t really have any other way to acquire customers, so you just have to eat it
you hear this comparison every now and then where someone will talk about, “Oh, Shopify’s attach rate code for take rate is called 3%, what a discount versus Amazon”. My first remark is, “No, no, no. The Shopify merchants have to wake up and go find customers [on FB/Google] everyday and they quickly end up being above the 20% take rate of Amazon.”

You mentioned in passing in your note that there’s no traction for Shopify Audiences. This is the Shopify Plus of offering where merchants can aggregate all their data to better buy ads on Facebook, it was a bit of a response to ATT, I was hugely in favor of this, I actually wrote they should do this. They announced it a week later, which to be clear it wasn’t because I wrote it, we were thinking on the same wavelength. You said there’s not really much take up for it, what’s the hold up or why is it not resonating?

MM: So the thing with Audiences, I think we should pull back. In 2021 and 2022, as you’ve talked about, ATT changes, it becomes harder to find your customers. At the same time, there’s global logistics issues. Shopify set out to solve two of the main pain points for their merchants, fulfillment and traffic acquisition. Fulfillment is incredibly hard and scale matters, they divested it. Audiences is the other attempt to solve for one of the headaches. The way it works is merchants share data and you create lookalike audiences. Shopify has been rolling it out with some of the larger merchants, they have very promising ROAS. I think during the same time Meta got really good with working with the new construct in which they’ve been forced to operate, same with Google, and they’re just almost always going to win that, basically.

Basically, Meta and Google got better faster than Shopify Audiences to make a difference.

MM: Yes, they got better faster. I don’t think merchants are really interested pulling their data if you’re a sneaker company. Yeah.

Right. Even if they were close, “Look, oh, you’re saying this option, I don’t have to share my data to everybody else. I’m going to go over there.”

MM: To be clear, Shopify disagrees with my perspective on this, but I also, I’m not pulling this out of the air. When we did the upgrade, I spoke to a bunch of people who run Shopify Plus agencies and I was also just at an e-commerce conference. I ask everybody who will take the time to talk to me, “Are you interested in Audiences? Are you using it?” I never get a positive data point that, “Hey, yes, we’ve been using it, amazing product”. I would love to see it be successful.
if you have a good brand, if you’re North Face jacket, how soon till Patagonia is buying North Face AdWords?

MM: It’s a ruthlessly competitive market and you hear from a lot of frustrated merchants. This business, the growth of Amazon’s advertising business is one of the most impressive things I think in all of consumer Internet in the last decade from effectively nothing to over a $40 billion run rate business, and there’s a lot of merchants who were in the marketplace five or more years ago when it was simply less efficient. There was this first mover advantage where the ad buyers weren’t all as sophisticated running as like off-site measurement tools and there was an arbitrage. Now, the floodgates have also opened to China-based merchants who are really sophisticated in the ad bidding and the margins have just compressed.
https://stratechery.com/2024/an-interview-with-michael-morton-about-e-commerce-winners-and-losers/

Amazon would change from being an e-commerce Aggregator to being an AWS-type of logistics business, with Amazon.com as its anchor customer.
https://stratechery.com/2023/meta-and-amazon-meta-shops-and-att-amazon-logistics-and-the-anti-amazon-alliance/
https://stratechery.com/2022/beyond-aggregation-amazon-as-a-service/

3PL platform
buy with prime
amazon advertising

ideas:
visualizing the target segments: ads are currently a black box. hotjar? train ads on hotjar data?
The brands contacted by The Times all said that they had no idea that their adverts were placed next to extremist content. Those that did not immediately pull their advertising implemented an immediate review after expressing serious concern.
fully ai generated feed for entertainmnet, can embed ads into 3d model
3d generated world model for metaverse (minecraft, roblox sufficient pixelated)
modularization of distribution of physical supply. what are the biggest suppliers irl?
Previously, publishers integrated publications and articles. Google modularized individual pages and articles, making them directly accessible via search
Previously, publishers integrated content and advertisements. Facebook modularized advertisements by allowing advertisers to target customers directly, not via proxy
Previously, book publishers integrated editing, marketing and distribution. Amazon modularized distribution first via e-commerce and then via e-books
Previously, networks integrated broadcast availability and content purchases. Netflix modularized broadcast availability by making its entire library available at any time in any order
Previously, networks integrated mass-market advertising and general interest programming. Snapchat (and many other services) modularized attention
Previously, taxi companies integrated dispatch and fleet management. Uber modularized fleet management by working with independent drivers
Previously, hotels integrated vacant rooms and trust (via brand). Airbnb modularized vacant properties by building a reputation system for trust between hosts and guests
how do u shop irl (physical | limited)? how do u shop online (images, trust | unlimited)? how does an agent shop online ( json? | generated (snapink)?)
What is the critical differentiator for incumbents, and can some aspect of that differentiator be digitized?
If that differentiator is digitized, competition shifts to the user experience, which gives a significant advantage to new entrants built around the proper incentives
Companies that win the user experience can generate a virtuous cycle where their ownership of consumers/users attracts suppliers which improves the user experience
fb claims theyre connecting us but interfacing thru phone means personalization and atomization. whereas collective discovery for masters in japan is actual collective exp bc phoneless
performed vs conducted behavior newsfeed, studio ghible ‘creative’
google circles login with localn chat gpt
tradition of advertising has been random shit (ppl yelling in a bazaar, ozempic on the sunday newspaper, etc) flatlining ~1.2% of GDP. it got a boost when coupons were introduced into physical products like cereal boxes and magazines. But with TV, attention and storytelling were upgraded culminating with saquan barkeley/influencers level relevance. however the best ads have always been search, bc you catch users at moment of intent (affiliate, ‘organic’ how-tos). amazon goes a step further and can not only display your product but allow for the converting/purchasing (+ “PERFECT” attribution tracking for the very ROAS metric driven industry). GenAI can do personalization+timing?
https://stratechery.com/2016/the-reality-of-missing-out/
Television news, on the other hand, increasingly gained a quality and credibility that the newsreel lacked. The information came first from a familiar anchor, with footage used for the most part as a substantive complement rather than purely as an eye-catching distraction. Indeed, the familiarity of the network anchors helped television news surpass not only the newsreel, but also the newspaper, as the main source of news…
](https://stratechery.com/2020/never-ending-niches/)
The Rise and Fall of American Growth
automatically add a user to a messenger/ig group chat if they bought something on the platform? Awareness->consideration->conversion->loyalty
local search (the most common category of search). geoguesser open data?
retail stores – free TV display, in exchange for ad space inventory. digital shelf label? in-store radio music specific voice ads?
Whoever can really prove incrementality for these low volume growth industries like CPG wins an enormous advertising pool of dollars. The challenge is the CPG companies are sophisticated and they don’t want to pay for transactions that were already going to happen. I always use the example, I consume an unhealthy amount of peanut butter every year and no peanut butter company wants to make themselves pay for Mike’s peanut butter purchase because they know I was going to buy that brand anyways. Walmart is the one that has the ability to show that. “Look, we need inventory, we need places to put ads” — it’s like the pre-ATT world on steroids, “We can track to the cent this ad resulted in this lift and that’s really compelling”.
Reciting this history in the context of Walmart made me realize there might have been another casualty to ATT: for a long time both Google and Meta were expending a lot of effort trying to build out advertising offerings that tied together digital ads and offline conversions; while these initiatives were never broken out as distinct products in earnings, I do recall a lot of time being spent on earnings calls discussing the possibilities. Neither company has mentioned these initiatives for a while, though, and I wonder if ATT is a reason why: the challenge with tying together digital ads and offline conversions is that it is a fundamentally probabilistic based exercise, and maybe it’s just too challenging now (or maybe all hands needed to be on deck to fix digital conversions first).
https://stratechery.com/2024/walmart-earnings-walmart-connect-and-closing-the-loop-walmart-acquires-vizio/
grocery store shopify - local perishables. something amazon is banned from doing and walmart is doing well
applovin for in app ad optimzation?
Notably, Apple’s alternative for app install ad campaigns, SKAdNetwork, is so limited that there is likely to be tremendous value in whatever company can create the exact sort of automated campaign creation that Facebook is already offering.
destination sites are direct urls that dont require intermediation by google/fb (nytimes, nike, etc). they are based on the promise of focus and quality. in the future we will have quality solved, all that is left is focus & discovery of such.
link their Amazon and Meta accounts
The pay-off is, once gain, a win-win: Meta gets better tracking, and thus better performing ads, while Amazon gets more volume, both as a first order concern in terms of Meta ads, but also as a second order in that letting Meta have the key data will drive even more conversions in the long run. Remember that for all of Amazon ads’ outsized success they are still about skimming money from intent-based search: Meta excels at finding people at the top-of-the-funnel, and introducing them to products they didn’t even know they wanted.
sign in with chatgpt/apple/google. 27:34+ | ecommerce commission, recommendation on flight/shoes https://stratechery.com/2025/an-interview-with-openai-ceo-sam-altman-about-building-a-consumer-tech-company/#:~:text=be%20the%20differentiator%3F-,SA,-%3A%20Where%20I
fb glasses pass test. just need to be <$1000> https://stratechery.com/2024/an-interview-with-meta-cto-andrew-bosworth-about-orion-and-reality-labs/#:~:text=appearances%20actually%20matter.-,AB,-%3A%20To%20his
walkie talkie, radio, ‘always on’ ai voice (news, police, entertainment, traffic)
agentic ad attribution? https://www.prorata.ai/
AR <-> iphone interopitability. ” I want to listen to gaga” - idc what platform… have to b logged in? everyone focused on headless browser but thats so web1.0
english-english translation. the future moat will be explination of (capital +) idea you want executed. how to collect stream of consciousness? human chain of thought?

I don’t know the exact numbers, but I’ve heard rumors that Google is spending a billion dollars this year on generating new training data, and if you’re going to spend billions and billions on your CapEx to build out your GPU training clusters, spending some fraction of that or maybe an equal amount in generating data, which is a kind of CapEx as well kind of makes sense. Someone told me the other day experts are the new GPUs and so there’s this wave of spending on experts who are going to generate tokens that can be valuable.
https://stratechery.com/2023/an-interview-with-daniel-gross-and-nat-friedman-about-the-ai-hype-cycle/
dispatch wrappr for all things that has ur best intererst https://www.crosshatch.io/

studio ghible ‘creative’
connection to reality, feeling in control, blank page prompt (“just generate something”)
product company: constrain your product, dont just open your api for general use.

mount rushmore
Clayton Christensen
Ben Thompson (Stratechery)
Chris Dixon
Bill Gurley
John Carmack

Appendix

Revenue Sharing

from chatgpt Here’s an updated, corrected list with authoritative links for each entry. I’ve also added major app stores, game storefronts, crowdfunding sites, and mod marketplaces you may have missed:

Platform	Platform Take (%)
Spotify	30% (ChartMasters)
Apple Music	30% (ChartMasters)
Amazon Music	30% (ChartMasters)
Tidal	30% (ChartMasters)
YouTube (Partner Ads)	45% (Google Help)
Patreon	8–12% (Wikipedia)
Nebula	50% (Fast Company)
Meta – Fan Subs	Up to 30% (Log in or sign up to view)
Google Play Store	15% on first $1 M; 30% thereafter (Google Help)
Apple App Store	15% on first $1 M; 30% thereafter (Apple Developer)
Netflix	N/A – content licensed via fixed deals (Giro’s Newsletter)
Twitch (Subs)	50% (Affiliates) / up to 70% (Partners) (Twitch Help, TechCrunch)
TikTok – Ads (Pulse)	50% (Wikipedia)
TikTok – Live Gifts	77% (FXC Intelligence)
Instagram Live Badges	0% (creators keep 100%) (Log in or sign up to view)
Instagram IGTV Ads	45% (WIRED)
Amazon (Commerce)	~15% average referral fee (App Radar)
eBay	~10% final-value fee (App Radar)
Etsy	6.5% transaction fee (App Radar)
Shopify Payments	2.9% + $0.30 per tx (App Radar)
PayPal	2.9% + $0.30 per tx (Reddit)
Stripe	2.9% + $0.30 per tx (Reddit)
Square	2.6% + $0.10 per tx (Reddit)
Visa/MasterCard	~1–3% interchange fee (App Radar)
Airbnb	~15% combined guest+host fee (App Radar)
Uber	~25% commission (App Radar)
Lyft	~20% commission (App Radar)
DoorDash	~30% commission (App Radar)
Grubhub	~30% commission (App Radar)
Booking.com	~15% commission (App Radar)
Expedia	~15% commission (App Radar)
LinkedIn (Ads)	~30% ad platform take (App Radar)
Snapchat (Spotlight)	50% (Twitch Help)
Pinterest (Ads)	~30% ad platform take (App Radar)
Steam	30% standard cut (Wikipedia)
Epic Games Store	12% standard cut (100% for First Run) (Epic Games Store, CG Channel)
itch.io	Developer‑set (default 10%) (Wikipedia)
Unity Asset Store	30% (Wikipedia)
Unreal Marketplace	12% (Unreal Engine)
Kickstarter	5% + payment fees (Wikipedia)
Indiegogo	5% + payment fees (Wikipedia)

from claude:

Platform Revenue Capture Percentages by Industry

Content Streaming & Digital Media

Platform	Revenue Captured	Source
Spotify	~30%	PrintifyBlog
YouTube	45%	FameFuel
Apple Music	~30%	Digital Music News
Amazon Music	~30%	Digital Music News
Tidal	~30%	Billboard
Netflix	~70%	KFTV
Twitch	50-60% (standard), 30% (for Plus Program)	Twitch Blog
TikTok	95-98%	InfluencerMarketingHub

Creator Economy & Subscription Platforms

Platform	Revenue Captured	Source
Patreon	5-12% + payment processing	Patreon Support
Nebula	~30%	Nebula
OnlyFans	20%	OnlyFans
Substack	10%	Substack
Ko-fi	0% (5% for memberships)	Ko-fi Help
Buy Me a Coffee	5%	Buy Me a Coffee

App Stores & Software Distribution

Platform	Revenue Captured	Source
Apple App Store	30% (15% for small developers)	Apple Developer
Google Play Store	30% (15% for first $1M)	Google Developer
Steam	30% (reduced rates for high volume)	Steam
Epic Games Store	12%	Epic Games
Microsoft Store	15% (apps), 12% (games)	Microsoft

Social Media & Digital Advertising

Platform	Revenue Captured	Source
Meta (Facebook)	55-70%	TubeFilter
Google (Ads)	68-70%	WordStream
Instagram	55-70%	EMarketer
Snapchat	50-70%	Snapchat for Creators
Pinterest	60-70%	Pinterest for Business
LinkedIn	60-70%	LinkedIn Marketing
Twitter/X	50-70%	Twitter Business
Reddit	60-70%	Reddit Advertising

E-commerce & Marketplaces

Platform	Revenue Captured	Source
Amazon (Commerce)	8-45% (varies by category)	Amazon Seller Central
eBay	5-15%	eBay Seller Center
Etsy	6.5% + $0.20 listing fee	Etsy Help
Shopify	2-3% + transaction fees	Shopify Pricing
Airbnb	3-14.2% (host fee) + 5-15% (guest fee)	Airbnb Host Resource Center
Uber	25%	Uber Driver
Lyft	20-25%	Lyft Driver
DoorDash	15-30%	DoorDash Merchant
Grubhub	15-30%	Grubhub for Restaurants
Booking.com	10-25%	Booking Partner Help
Expedia	10-25%	Expedia Partner Central

Payment Processors

Platform	Revenue Captured	Source
PayPal	2.9% + $0.30 per transaction	PayPal Fees
Stripe	2.9% + $0.30 per transaction	Stripe Pricing
Square	2.6% + $0.10 per transaction	Square Pricing
Visa	1.4-2.5% per transaction	Visa USA Merchant
MasterCard	1.5-2.6% per transaction	Mastercard Merchant

Software as a Service (SaaS)

Platform	Revenue Captured	Source
Salesforce AppExchange	15-25%	Salesforce Partner Program
Adobe Exchange	15-30%	Adobe Exchange Program
Oracle Cloud Marketplace	15-30%	Oracle Partner Network
Microsoft AppSource	15-20%	Microsoft Commercial Marketplace
Zoom App Marketplace	15-20%	Zoom Marketplace
Slack App Directory	20%	Slack API

Notable Outliers with Low Revenue Capture

Platform	Revenue Captured	Source
Ko-fi	0% (5% for memberships)	Ko-fi Help
GitHub Sponsors	0%	GitHub Sponsors
Bandcamp	10-15%	Bandcamp for Artists
Gumroad	10% or $10/month + 3.5%	Gumroad Pricing
Epic Games Store	12%	Epic Games

Trends by Industry (Average Revenue Capture)

1. Music Streaming: 30% (platforms keep 30%, pay out 70%)
2. Video Streaming (YouTube): 45% (YouTube keeps 45%, pays out 55%)
3. Social Media & Advertising: 60-70% (platforms keep 60-70%, pay out 30-40%)
4. App Stores: 15-30%
5. Ride-sharing/Delivery: 20-30%
6. E-commerce Marketplaces: 5-15%
7. Creator Platforms: 5-30%
8. Payment Processing: 1-3% + fixed fees
9. SaaS Platforms: 15-30%

This data reveals significant variations in how platforms monetize across different industries. Music and video streaming platforms tend to have more standardized revenue share models (typically 30-45% for the platform), while social media platforms capture a much higher percentage of advertising revenue (60-70%), leaving creators with a smaller share. Payment processors and creator-focused platforms tend to have the lowest revenue capture rates.

References

a
https://stratechery.com/2024/friendly-google-and-enemy-remedies/
https://stratechery.com/2024/enterprise-philosophy-and-the-first-wave-of-ai/
https://stratechery.com/2020/the-end-of-the-beginning-follow-up-technological-revolutions-and-financial-capital-venture-versus-productive-capital/
c
https://stratechery.com/2017/ad-agencies-and-accountability/
d
https://stratechery.com/2023/ai-and-the-big-five/
e
https://stratechery.com/2022/instagram-tiktok-and-the-three-trends/
f
https://stratechery.com/2024/the-gen-ai-bridge-to-the-future/
g
https://stratechery.com/2019/shopify-and-the-power-of-platforms/
h
https://stratechery.com/2024/perplexity-and-robots-txt-perplexitys-defense-google-and-competition/
i may 2024 https://stratechery.com/2024/the-great-flattening/