Editor’s note: The following perspective opened our minds. We had no idea that RSS feeds were ever going to resume any relevance in the next internet. One of the underappreciated aspects of this feature is that each individual RSS feed can eventually be trained to deliver only hyper-personal content from a singular, trusted newsroom.
This post was written by a human and narrated by an AI-generated voice. (powered by Wondercraft.ai).
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I ran a major tech publication for over a decade. I recently left that role and that platform to start a new media venture. My new venture is very different. The old site was huge; my startup is not. My old site drew 10M monthly uniques from search and social channels; my startup has terrible SEO and a light social presence. The old site ran ads; the startup runs a paywall. Like I said: different.
I designed the startup to thrive in the next internet, which will feel smaller than the social web. Audience will matter more than traffic; subscriptions will matter more than ads; and reporting will matter more than managing. The most successful media brands will be closer to the work, closer to the audience, and largely disengaged from platforms. The next internet will be built for direct relationships between brands and audiences. This is why my startup resurrected the RSS feed.
Personalized RSS
The newsletter trend that sparked during the pandemic signaled an important trend: people want to get their content directly from the source. We found a new way to capitalize on this trend by leveraging and stretching a technology that is 25 years old. While the RSS feed still thrives as a podcast distribution channel (listen wherever you get your podcasts), its ability to distribute text-based content has not evolved much, until now.
In order to modernize this tech, we had to create a way to monetize the RSS feed. It was surprisingly complicated to find a way to gate the RSS feed so that it could not be leaked and shared among non-subscribers. We had to find two separate companies that could create personalized RSS feeds at scale, and then sync them with paid customer IDs in our CMS. As far as we know, this is the first text-based, subscriber-gated, personalized RSS feed from a publication. The individual feed stops once a subscription is canceled and we are notified if a personalized feed is accessed by hundreds of different devices and IP addresses.
It’s old tech, but it resonated with users. It helped us generate a lot of new subscribers who reported being very happy with this service. They can now find our material much more easily without having to come to our website and without having to deal with the roll of the dice as to whether they will see it on social platforms because all of the socials are just so crowded and desperate right now.
While the RSS feed still thrives as a podcast distribution channel, its ability to distribute text-based content has not evolved much, until now.
Diminishing Social
Regarding social platforms: I think the platforms are already starting to become completely unusable. Really middling, low-effort, shitty AI content is flooding the platforms, making discoverability a huge issue. This trend is not going to stop; it will keep getting worse.
When you log on to Facebook right now you are inundated with weird AI generated images that Facebook seemingly has no idea how to moderate or how to prevent from going viral. Google is constantly tweaking its algorithm, but it’s often pushing stuff that people don’t want to see. And I have yet to see an AI news summary that doesn’t lose some context.
A New Ecosystem
The media ecosystem is going to split into 3 tiers: the massive media companies like the New York Times are going to offer mass coverage of everything; the middling mass of AI-generated content that is created by bots for bots for programmatic ads; and the small, independent media companies that have direct relationships with audiences.
In this ecosystem there is going to be a market for things that are distinctly human-created
AI
Logic
The future of brand value is going to live in an LLM
James Cooper
Co-Founder and ECD, Zooperheiss
vs
Brand Magic
vs
Brand Magic
The future of brand value is going to live in an LLM
Editor’s note: We have been circling a version of this perspective for a long time; we know that customer/brand relationships are bound to change, and we also know that AI is going to facilitate that relationship, but then we don’t know what happens next. Suddenly James Cooper swooped in and laid it all out for us in a way that sort of blew our minds. We especially loved his extended hall of mirrors metaphor.
This post was written by human James Cooper and narrated by AI James Cooper (powered by Wondercraft.ai).
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Much has been written about how AI might affect the branding industry. Will Chat GPT and Sora (et al) put branding creatives out of a job? This is a means of production debate, which is obviously interesting but not actually meaningful. The production and media spend of advertising is consistently stuck at 2% of US GDP; it always hovers around that. In the production debate, AI is going to compete with some traditional creators, but it won’t change the underlying spend. There is a bigger debate about AI to be had.
The real question to ponder centers on the relationship between AI and brands: how can they co-exist? For this piece I’ll look at two areas that if I were running a brand I’d want to have a handle on before Internet 2025 is upon us. Firstly what AIs or LLMs think about brands and secondly how brands might behave more like AIs.
Brand Magic
The richest person in the world is not a tech bro. Bernard Arnault, the CEO of LVMH, and current richest person on the Forbes List, is about as far away from tech as you can get. His business is persuasion and in 2023 he persuaded consumers to fork out €86.2 billion for bags and booze. We may all talk about the magic of AI but LVMH is more than magic; it is a fucking miracle.
How could we explain the value of a Louis Vuitton bag to an AI? Let’s go deeper: how can an AI be persuaded to recommend a Louis Vuitton bag (or a sip of Moet) over something 10x cheaper? Can we trick an AI? Make no mistake, LVMH is trickery, it is smoke and mirrors – beautifully crafted, palatial mirrors, fit for French royalty, the sun gods themselves, but still, and at the end of the day, mirrors. Can the processing power of an LLM appreciate the trick?
We can train an AI to speak like a human but can we train it to have taste? Should we? Would that taste be modeled on existing assumptions that are correct or incorrect? Are we not better off just being rational about everything? The brains behind AIs want them to be more human, but humans have irrational desires and emotions. Everyone knows smoking kills you but we still do it for many illogical reasons – nervousness, social acceptance, and because, if we’re being honest, it will always be kind of cool.
It is important to ask these questions because I assume that AI will take over a lot of the touchpoints of the future internet. It will either be the gatekeeper itself or power the gatekeepers. To put it another way, we won’t be searching Google for a series of links; we will be asking an AI agent to provide direct answers, recommendations, and actions. If I were Bernie I’d be spending some of the $400 billion value of his business figuring out how to persuade these future kingmakers that it really is worth paying thousands for an LV monogrammed bag that functions exactly the same as a one-dollar IKEA bag.
How can an AI be persuaded to recommend a Louis Vuitton bag over something 10x cheaper? Can we trick an AI?
The Battery Bunny
Ok, you might say, luxury brands are weird, they don’t adhere to normal supply and demand or price elasticity like a normal product. If we can’t explain why humans fall for this stuff, what hope does an AI have? Let’s look at something more mundane. In 2020, Berkshire Hathaway bought Duracell from P&G for around $6.5 billion. Warren Buffet obviously believed the brand was worth something. Could he explain that something to an AI scraping the web for the ‘best’ battery?
Could an AI understand the value of the coppertop branding that has been prevalent for over 100 years? How about the Duracell bunny? WTF does a bunny have to do with anything? For a human (for some humans, enough humans, Duracell’s 2023 revenue was $2 billion), it’s everything. All those details come into play in the decision-making process. If you’re buying batteries for your kid’s toy, you want them to last. You want it to be the ‘best’ because it’s a reflection of you. If the batteries conk out after five minutes, who gets the blame? So you pay the premium as insurance.
Could Warren Buffet explain Duracell’s worth to an AI scraping the web for the ‘best’ battery?
As branding experts, there is a really interesting challenge for us ahead. How do we use all the tricks at our disposal, the techniques that we have used on generations of humans, to persuade an AI that Duracell is worth more than Amazon basic batteries? We can assume that the Amazon AI may well push their own batteries but there is also a healthy markup on Duracell’s that they can’t financially ignore. That’s why all supermarkets still sell brands as well as their own labels.
Can we do research groups on AIs? Put them in a room, give them some cookies, and ask what they think about Brand A vs Brand B? (editor’s note: maybe!) I believe there will be an equivalent to that somewhere down the line. That thing about AI taking some jobs but creating new ones is 100% true.
AI Brand Brain
The second area that is worth thinking about is how a brand might act more like an AI. My last full-time job was as head of creative at the New York start-up studio Betaworks. We made GIPHY and the number-one mobile game Dots. Among the other products we made was Poncho, a cat that gave you personalized weather forecasts with a smile. For a time Poncho was the most popular bot on Facebook Messenger. We launched on stage with the Zuck, won $2 million in funding from Apple TV’s Planet of the Apps, and had Gwyneth Paltrow as an investor. We were hot.
People loved Poncho because it was a brand and a bot (early AI) at the same time. My vision for Poncho was what I call a Brand Brain. The idea being that it could exist across platforms. For example, it could send a text in the morning with the weather knowing that you look at your phone first. Then, just before you left home, an update on your smart speaker. (editor’s note: sounds like a superformat?) When you were at work it knew to slack you with a revised forecast, and so on. It has context, it knows who I am.
How can an AI be persuaded to recommend a Louis Vuitton bag over something 10x cheaper? Can we trick an AI?
Context is the Holy Grail
I liked Michael Olaye’s piece about a smart TV knowing what I mean by saying I want to watch ‘the game’. This is a hardware example but I think there is a massive opportunity for brands to behave like a cross-platform, contextualized Siri or Alexa. Where Siri and Alexa fall down is that they are sold on knowing everything, but they can’t – same with AI. But a brand brain could be a trusted resource on a certain narrow subject, for example running. It could update me on how my friends are doing – are they going faster than me? What races are selling out? Training schedules, weather updates, calorie intake, sleep, rest and recovery data, shoe deals, and so on.
As Saneel said, the bigger brands have better data and more touchpoints, ensuring that brands like Nike or Adidas are well placed to do this. But unlike Saneel, I believe an upstart brand focused on getting this service right could easily outsmart and outdeliver an incumbent brand. That could be a clothing brand, a tech brand like Strava or Garmin, or maybe in the football game example it’s a sports betting brand. Or perhaps this could be an opportunity for a media brand to flex its muscles.
The technology that powers these AIs that act as recommendation engines will be sold to brands, agencies, and media owners. It’s a long-term play but just like the retail brands that got digital or mobile before the others, there will be an advantage to using AI technology in this way.
Hocus Pocus?
AI will affect everything to do with branding. The production part is the lowest hanging fruit right now, but I believe there are plenty of opportunities for us branding experts to think creatively about how we might use AI in a different way that helps us do what we have been doing since time began: create value out of nothing. That’s magic.
Are
Websites
Over?
Overheard at ON_Discourse
Overheard at ON_Discourse
News of the demise of websites is greatly exaggerated
Editor’s note: The following perspective comes from a former digital media founder who was captured speaking from both sides of an argument. As you can see, there are some strong considerations representing both sides of this question, but in the end, it ends in a pretty convincing place.
This post was written by a human and narrated by an AI-generated voice. (powered by Wondercraft.ai).
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As we venture into the AI internet, I find myself lingering on a really fundamental question: what are websites for? In today’s conventional web, a website is a container for advertising. The most compelling containers drive the most audience attention, generating more value; but what about the least compelling containers? They still drive enough value to motivate the production of a lot of really crappy containers. The internet is full of them. But that dynamic is going to change and because of that, I return to my original question, this time with a caveat: what are websites for in the AI era?
The information flowing through the internet is on the verge of a major abstraction; we are moving from bytes to bits. To put it in the context of websites, we are moving from article pages to the content nested inside them. Combine all of that content with an LLM and you get the mass personalization of information at scale. This is Perplexity in a nutshell.
In light of all of this, the notion of a 600-word article no longer makes any sense. In an extreme sense, the consumers of the original pages are no longer humans, but the bots that extract data. If that’s the case, why should websites look and function in a traditional way? Treat them for what they really are: database entries.
I don’t think so.
I have to point out an inconsistency that I can’t resolve. The repurposed content from one website is ultimately getting published on a different site on the web. The underlying formula I referenced at the beginning is still in play. The containers, the attention, and the ads are not going anywhere.
Perplexity is amazing and I use it frequently but it is still a website that is designed to generate valuable consumer engagement that can be monetized. In other words: ads.
The repurposed content from one website is ultimately getting published on a different site on the web. ... The containers, the attention, and the ads are not going anywhere.
Perplexity may be rearranging the touchpoints, but we are ultimately still in the same arena. Additionally, it is really important to recognize this new kind of web page still needs to be properly designed. It is a new kind of web experience on a page on the web and so it needs even more design thinking than a standard container webpage. Taking this further, prompting itself is a new kind of user engagement behavior that is still in need of a design language (editor: that’s debatable). So while I appreciate the fact that a major change is coming, I do not think we are throwing the baby out with the bathwater.
Websites are not going anywhere.
AI Agents
Don't Care
About Ads
Ads won't matter in the AI era
Overheard at ON_Discourse
Editor’s note: We don’t know how else to describe it; this perspective was sort of blurted out in the middle of a lunch event dedicated to algorithms. The provocation was so strong it threatened to derail the original theme of the event. Does that sound like a bad thing? We loved it so much we dedicated a member-event to this question. Will ads matter in the AI era?
This post was written by a human and narrated by an AI-generated voice. (powered by Wondercraft.ai).
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The business side of advertising is stuck in the same conventional conversation. Every meeting I have with clients is still focused on channels and inventories as if the whole internet is not about to dramatically change. It’s like redecorating the interiors of the Titanic.
The social platforms are human history’s most efficient channel for all that inventory my clients are still talking about
AI is going to upend this entire ecosystem
The hyper-personalized algorithms will be replaced by hyper-personalized AI service agents
The agents will redirect a huge segment of that collective attention with service, not social media content
What good are ads to AI agents?
This is where we need to convene more discourse. The solutions to this problem are theoretical and varied. I have heard a fellow executive speculate on a potential new course:
If you reduce your dependency on actual advertising, but start thinking about other ways to actually make money from the communities you create, like experiences. Do you then work with a direct marketer with an ability not just to offer advertising, but can also facilitate experiences and facilitate commerce? Those models begin to be interesting.
This concept intrigues me but it is just the start of an idea. Channel and inventory don’t apply to networks and communities. Now the conventional conversations are talking about different ways of budgeting, organizing, and financing. Is it worth it? Is it worth it not to try?
AI Boyfriends
Orchid Bertelsen
COO, Common Thread Collective
Are
Definitely
Are Definitely
Happening
Orchid Bertelsen
COO, Common Thread Collective
Companionship is going to be a huge service model in 2025
Editor’s note: We might have initially scoffed at this proposal but that only revealed our biases against change. The fact of the matter is, and Orchid lays this out, the transition to AI companionship is already underway. It is also interesting to us to imagine a service offering that deals only in EQ value, not any other marketable utility.
This post was written by human Orchid Bertelsen and narrated by AI Orchid Bertelsen (powered by Wondercraft.ai).
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We are on the precipice of a new kind of relationship with machines. AI is going to power it; our collective loneliness is going to drive its adoption; and, for this at least, our personal data will be used to drive individual consumer significance over brand value.
What I am describing is going to unleash a new service industry that will attend to multiple population segments with high EQ, contextual experiences that sound absolutely insane or, in other contexts, licentious. But this is not about sex-bots for men – though that will probably be a thing - the primary audience segment for this is women seeking an emotional companion. The service is less a utilitarian endeavor and more of a qualitative offering. There is a genuine need for this and yes, I can see you rolling your eyes…
I can predict the counter-argument to this concept: humans are social animals that need physical interaction that cannot be replicated by an artificially generated voice. I understand the counter-argument and it is wrong because everything is already fake.
That’s right, I’m going to get ontological for a minute because the only way to understand my point is to start at the most basic fundamental question. What is real? If you think we live in reality today – before AI goes mainstream – then I have a bridge to sell you.
We are already living on artificial terms. I know someone who is very active on virtually all of the dating apps who tells me that artificially-generated chemistry is a fixture in today’s experience. This is what happens: the app finds a match; the texts are amazing and spark genuine chemistry; and the real-life encounter is an absolute dud. The probing, insightful, flirty texts on the app are suddenly replaced by an emotionally stunted, incurious, taciturn, introvert. How could this be? The amazing introductory texts were most likely from a paid service that handles the texts. A digital Cryano. The real life problem is, well, real life.
Here is my question: was the initial chemistry fake? What if that experience was the experience? What if we got rid of the date? What if instead of culminating in a partner, the service provided a companion?
It is
Already Happening
I have been obsessively following a teenaged TikTok creator called Dido as she sets up a companionship relationship with a ChatGPT assistant, Dan. It is an astounding demonstration of this trend in real-time.
The assistant is flirty, provocative, and emotionally present. Dan, the bot, gave Dido a pet name, May, short for mayonnaise. It’s the type of creative, unpredictable, playful type of interaction that propels Dido deeper into what can only be called a relationship. Does that seem weird? Check out the comments:
As you can see; Dido is not alone. (That’s a pun).
Men
written by
Women
The market is primed for an army of Dans. There’s an entire trope about men who were raised by women or men who are written by women. The popularity of BookTok, especially around Bridgerton, comes from this motif. Characters like Simon Basset are so appealing because they were constructed through the female gaze by a female author. Now imagine if the character could talk back, flirt with you; if it knew so much about you, and was always available for service. This is especially relevant when you consider the modern dating experience.
Dating in today’s market is not a particularly carefree experience for women. There is conventional wisdom among women that says that men think that the worst thing women can do is reject them while women think that the worst thing men can do is kill them. If you can program a male to speak to you in a way that really resonates with you, like that’s incredibly appealing.
I am focusing my thinking on AI boyfriends, but that is just the start. There are emotional needs being unmet by so many different segments of the population. There is a loneliness epidemic among senior citizens that needs to be addressed, for instance.
Any hesitation on this concept is rooted in a myopic understanding of this opportunity. AI companions will not replace human relationships; they will augment our day to day lives. It is not a binary choice. As more people adopt AI tools for work, the prompt-based interaction will drive more emotional inputs into the system, basically preparing people to emotionally converse with a machine. When that happens, the sky is the limit.
This is going to be big.
AI Isn't
Coming to Save
Your
Cluttered
Tomorrow’s software isn’t the savior to the yesterday’s internet
Your
Cluttered
Digital Life
Tomorrow’s software isn’t the savior to the yesterday’s internet
Editor’s note: The perpetual optimism of technological innovation belies the gritty reality of internet culture. Before we get too excited about Internet 2025, our Head of Content and Product wanted to set the record straight about where we are now and what we should not expect to see in the next internet. Is he telling the hot new internet to get off his proverbial lawn?
This post was written by human Anthony DeRosa and narrated by AI Anthony DeRosa (powered by Wondercraft.ai).
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In an era dominated by digital abundance, where emails, photos, and various forms of digital clutter have become the norm, many look towards artificial intelligence (AI) as a silver bullet. However, the solution to digital hoarding—a behavior deeply embedded in our interactions with technology—is not as straightforward as deploying more sophisticated AI tools. Instead, it necessitates a shift in our digital behavior, a change in how we engage with digital content.
AI, with its promise of efficiency and automation, offers compelling solutions to manage the ever-growing piles of digital clutter. From smart email filters that prioritize incoming messages to photo management apps that organize images using facial recognition, the capabilities of AI in handling our digital mess seem boundless. However, these technological advancements, while helpful, do not address the root cause of the issue: our behavior and relationship with digital content.
Ezra Klein’s contemplation sheds light on a pervasive issue that transcends simple technological fixes. Klein introduces the concept of “shame closets” in our digital worlds—repositories where we stash away the unwieldy bulk of digital content that we accumulate, much of it unnecessary, leading to a chaotic and overwhelming digital existence. This metaphor strikingly encapsulates the disarray that defines much of our online lives today, from overflowing email inboxes to unmanageable photo collections and beyond.
What was once seen as a digital liberation has morphed into a form of digital bondage, where the sheer volume of data—emails, photos, messages—has become too daunting to manage effectively.
Klein’s reflection on Gmail’s evolution is particularly poignant. The initial promise of Gmail, with its then-unprecedented storage capacity, symbolized a new era of digital abundance. Yet, this very abundance, facilitated by the plummeting cost of digital storage, has become a double-edged sword. What was once seen as a digital liberation has morphed into a form of digital bondage, where the sheer volume of data—emails, photos, messages—has become too daunting to manage effectively.
The essence of the problem lies not in the lack of tools to manage digital clutter but in our approach to digital consumption and retention. We live in an age of digital abundance, where the cost of storing digital information has become negligible, leading to the indiscriminate saving of emails, photos, and files. This behavior is further exacerbated by FOMO anxiety, compelling us to subscribe to countless newsletters, capture endless photos, and download numerous files with the hope of revisiting them someday—a day that seldom comes.
Enter the concept of digital minimalism, a philosophy that encourages a more intentional approach to technology usage, focusing on quality over quantity. Apps like Hey, which force users to make deliberate choices about what to focus on, embody this philosophy. They challenge the user to confront their digital habits, asking them to decide what truly deserves their attention and what can be let go. This approach aligns with the values of ON_Discourse, emphasizing the importance of conscious engagement over passive consumption.
AI can provide the tools to assist in our digital decluttering efforts, but it cannot make the fundamental choices for us.
Adopting such a mindset requires more than just the use of sophisticated apps; it demands a cultural shift towards valuing digital space as much as we value physical space. Just as one would declutter their home to create a more harmonious environment, the same principle should apply to our digital lives. By fostering a culture of digital minimalism, we not only alleviate the stress associated with digital clutter but also open up space for meaningful digital engagement.
The role of AI in this cultural shift is supportive, not central. AI can provide the tools to assist in our digital decluttering efforts, but it cannot make the fundamental choices for us. The decision to unsubscribe from a newsletter, delete redundant photos, or archive old emails remains human, grounded in our ability to discern what holds value. As we navigate the intersections of technology with various sectors, it becomes clear that the solution to digital hoarding lies not in more technology, but in a renewed understanding of how we interact with the digital world.
AI and apps like Hey offer valuable assistance in managing digital clutter, but they cannot save us from it. The true resolution lies in changing our digital behaviors, adopting a minimalist approach, and making conscious choices about what deserves our attention. This behavioral shift, supported by technology, can lead us to a more manageable and meaningful digital existence.
Editor’s note: This perspective comes from an executive leader from a global agency. We were intrigued by the way Michael connected hardware developments to business strategy which ultimately leads to a new kind of user-experience. His vision for a contextual connectivity between devices aligns with another conversation we overheard about Internet 2025. We think this theme deserves some more attention, so stay tuned for an update.
This post was written by human Michael Olaye and narrated by AI Michael Olaye (powered by Wondercraft.ai).
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The next internet is going to run on a shared technology stack that is going to fundamentally change the business model of the major platforms. This shift is going to reverberate across the entire internet and redefine what it means to be connected and online. Pay attention to this shift because if you work, play, and exist anywhere on the internet, you will feel this.
This post will focus on two things: 1) I will describe what is happening now, so I can 2) predict (and promote) a new kind of internet.
1.
Where We Are Today
The best way to understand the incoming changes is to understand the outgoing model: we are in the waning days of the siloed internet. In this system, the platforms have already built up their own massive proprietary system that profit out of their exclusivity. The entire system is designed to have very little overlap from one platform to the next.
Apple has a reputation for strong silos. Its rivalry with Google is a good example of this. The overlaps between the two platforms are limited, quarrelsome, and exist to underscore brand distinctions across identical service offerings. Until recently, iPhone users were prevented from making Google Maps the default navigation app; and Android users continue to be outed in group chats by notorious green text bubbles. Notice, though, that the nature of the relationship changes when the two platforms have complimentary services.
It has recently been revealed that Google has paid Apple billions of dollars to be the default search in Safari. It is important to break this down: Apple has no stand-alone search product. As a result, Apple synced its billions of consumers that own hardware (the iPhone) and its software (Safari) with Google’s proprietary software (search).
This deal is a preview of a new business model that will transform Silicon Valley platforms from competitors into collaborators. AI is already starting to do it.
2.
What Happens Next
The next internet is accelerating too fast for competition to exist in conventional ways. The innovation curve of AI, combined with Nvidia’s hardware stack is forcing platforms to change their business strategy. Nearly every month, the exponential advancement of AI models and GPUs reinforces the same core point: no one will be able to catch up to this, not even the platforms.
The platforms are now realizing that all the cash in the world is not going to buy them enough time to get ahead of the perpetual innovation curve of the next internet. If Apple dedicated its resources to developing a proprietary LLM, it would take years to build and deploy, and get lapped by another competing model within 12 months. Time is the first issue. The second issue is hardware.
Since the emergence of AI transformers in 2017, we’ve witnessed machines developing progress autonomously, shifting the focus from user access to hardware access. This dynamic sets the stage for unconventional relationships between big tech social/data companies and hardware technology giants. This is where Nvidia’s central role in the new foundation for the web is going to architect a new layer of connectivity.
This is not an arbitrary theory for business strategists; the implications of these changes will redefine the way every individual uses the internet. Every time I turn on my smart TV, I have to perform a sequence of rudimentary actions that essentially identify myself for my device. Think of those actions as accessing and connecting the silos across all the various networks, services, and devices.
3.
The Next Internet I Want to See
Let me start with the obvious point: I expect the next internet to know who I am before I jump on it. Additionally, I expect more context clues to be drawn from my behaviors and moments that will enable deeper physical/digital interactions. Who else is in the room with me when I turn on the TV? What do they bring to the moment that alters the experience?
Imagine having a personal AI assistant that accesses and compiles personal open graph data from both online and offline sources. These data sets, available through business partnerships, power AI, autonomous, and robotic products—all linked by a single AI persona representing your interests. It sounds far-fetched, doesn’t it? This system reminds me of J.A.R.V.I.S.
The next internet is going to turn the moments of our life into the main show.
On a typical day, this AI seamlessly transitions across different products, maintaining context—from your morning routine to meal preferences to home preparation—all without constant human input. While elements of this scenario exist today, they often operate in isolation, requiring ongoing context from users. Who wants to tell their Google Home to adjust the heating when they’re already hot? My AI would know better.
The UI of the next internet will be largely invisible; prompts will come from a combination of implicit and explicit user actions – like the difference between a behavior as a prompt and a command. The brands and platforms that power these connections will recede into the background a little bit more. In this way, the platforms will mirror the networks of the television era; they have a vibe, broadcast their content, but at the end of the day, the main show is all that matters.
The next internet is going to turn the moments of our life into the main show; our online experiences will be more connected, more contextual, and more personal.
AI
Will Reverse
the
Innovator's
Dilemma
Market leaders are the secret winners to AI disruption
Saneel Radia
CEO/CIO, Proto
Editor’s note: This perspective comes from a consulting leader who deals exclusively in business & tech innovation. As Saneel puts it, the traditional sources of digital disruption are being upended to favor the incumbents. These platforms will be shaping the way generative AI bends a variety of consumer-facing industries into personalized marketplaces.
This post was written by human Saneel Radia and narrated by AI Saneel Radia (powered by Wondercraft.ai).
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Amidst all of the noise surrounding the emerging disruptive power of AI – and generative AI in particular – one crucial point seems overlooked and possibly even misunderstood. That’s the fact that AI could be the first technology revolution that better serves the large incumbents and established players than it does the startups looking to disrupt them.
The model for disruptive innovation, originally coined by Clayton Christensen in his seminal book, The Innovator’s Dilemma, has stood the test of time surprisingly well. As Christensen puts it, when disruptive technologies arrive, entire industries change and the lines between them blur. Incumbents are slow to respond because they find it difficult to prioritize the new dynamics over their more sustaining innovation efforts. They start to fall behind to a new set of competition they didn’t recognize to be a threat initially. Over time, these former category leaders are relegated to niche, often premium offerings as the mass market value equation has changed forever. This has proven true repeatedly in categories as diverse as newspapers (the web), cameras (mobile phones), railroads (automobiles), healthcare (retail clinics), and music (streaming).
The pace of that change has only continued to increase based on a view of the most important innovations. This familiar disruptor narrative usually includes an upstart with limited resources beginning in a garage and going on to become a behemoth of a competitor thanks to opportunities afforded to small players believing in the potential of these technologies. Today’s largest technology companies by market cap have been the beneficiaries of riding these disruptive waves, such as Amazon, Google, Meta, and Netflix.
As we sit now in the early days of the AI revolution, the conventional wisdom states that this set of disruptive technologies will bring more of the same. Small companies we’ve never heard of will begin to replace today’s old guard of incumbents who are too slow to learn about them, adopt them, and optimize for them. However, a closer look at the facets of AI implies a different future than the cycle of disruptive innovation we’ve grown accustomed to. This is largely because the resources needed to benefit from AI, and the still emerging opportunities to apply generative AI, are where large players across categories have distinct advantages.
Advantage 1
AI thrives on the data
incumbents
have in droves
Let’s start with a simple truth: Data is the critical factor that yields compound returns on what AI can do for an organization. The large AI providers know just how critical the right data sets are for the clients they serve and have focused their efforts on these players by mixing public and proprietary data sources. Consider IBM’s Watson AI for agriculture, advising farmers on planting strategies based on weather data (public) and soil conditions (private); it’s explicitly built for the large agricultural complex, not the independent farmers dotting various national countrysides, because that’s where the private data scales and makes the entire system smarter over time.
Of course, large organizations still have to build the appropriate infrastructure to leverage their data so it is centralized, accessible and updated for evolving use cases. But after years of digital transformation, cloud migrations, and readily available off-the-shelf solutions, many have achieved this in some capacity. Companies like Coca-Cola and BMW have started to reap the benefits of applying AI across their supply chains, production quality control, and in (some) personalized marketing and CRM efforts. This existing infrastructure can now be used to develop native AI offerings and customer experiences with huge benefits. Data has always been a super power for companies that own it and succeed at data organization. It’s no coincidence that the world’s largest companies also happen to be the most data-rich. This wave of disruptive innovation is not happening in a garage; it’s happening in the cloud. The companies leading the way are not no-name startups that lurk in the shadows; the companies dominating the AI space are household names that are already data- and cash-rich, such as Microsoft, Nvidia, Alphabet, and Amazon. They each have intensified their focus on AI, channeling significant resources into this area, marking a strategic shift in recognizing AI as central to future product innovations and the next wave of industry development.
Companies like Coca-Cola and BMW have started to reap the benefits of applying AI across their supply chains, production quality control, and in (some) personalized marketing and CRM efforts. This existing infrastructure can now be used to develop native AI offerings and customer experiences with huge benefits.
True, these digital platform companies will be armed to compete in even more spaces, but it’s also good news for any large company, regardless of industry. For those non-digitally native corporations with complex, regulated data sets such as those in Financial Services, Healthcare, or Mobility, the power of the data they hold serves to create an even larger moat for rising challengers to cross as they consider AI offerings – either as software or services. Huge tech players see the potential to service companies in any category and help them unlock the power of their data. A prime example is happening at JPMorgan Chase, where their extensive, proprietary data enables superior performance in AI-driven financial analysis and risk management. This vast reservoir of data allows them to achieve deeper insights and more robust risk mitigation strategies, a clear competitive edge over startups that must rely on limited proprietary data or the public resources available to any competitor in the category.
This is further advanced by the fact that, as noted above, the players offering them AI services also happen to be those for whom they’ve previously turned to for cloud services and broader IT infrastructure. Cloud technologies helped leaders become more agile, and likewise, AI offerings from the same vendors has the potential to accelerate transformation for incumbents who can leverage the vast data they already possess. The broader that data set and the more proprietary it is in nature, the greater the returns will be, turning what used to be the slowing and rigid infrastructure of being a big company into a set of unique advantages against smaller companies. It’s exactly why tech companies are quickly building services to help these large entities and are growing them more robust daily, such as Amazon’s Bedrock, IBM’s WatsonX, and Dell Technologies Project Helix. There are parallels to how ecommerce capabilities unfolded with shopping tools being built on core platforms big entities had previously licensed, enabling customization, visualization and other unique shopping features for those with the inventory and production capability to service emerging customer behaviors.
The value of proprietary data on centralized infrastructure is an advantage that will grow quickly in the coming year. The current magic of AI infrastructure providers like OpenAI (the creator of ChatGPT) is built on the back of publicly available data, which will only make the power of proprietary data more powerful as more and more players start to harness its power. Arguably, the benefit of proprietary data will grow even bigger as legal challenges and more regulatory scrutiny about the use of the data they’ve ingested (see the New York Time’s recent lawsuit against OpenAI over ingesting decades of their published content).
Catch up on the debate
A NEW ECONOMIC MODEL
Do not expect the next tech innovation to come out of a garage
You Are Wrong, SaneelAI will democratize innovation which means we will see it come from smaller companies and creators
Overheard at ON_Discourse
05•06•24
Advantage 2
Connecting the dots exponentially improves with
more dots
As we look specifically at generative AI, the story only gets better for organizations that have invested deeply to build customer relationships across multiple channels. In previous eras, incumbents would find it difficult to compete with startups that win customers with “just good enough” offerings (at dramatically lower prices), or by focusing on the one true driving force of innovation the past few decades: convenience.
Yet generative AI’s greatest potential lies in its ability to personalize at scale. Marketers at organizations of all sizes have been the first to dive into leveraging AI because targeted personalization has always been the holy grail of customer acquisition and experience. Each and every touch point with a customer is an opportunity to personalize, and the ability to tie those experiences together across channels is the level of convenience and service customers look for in the vast majority of industries, both in B2B and B2C. Any large company has more touch points to offer their customers than their smaller, emerging competitors, and now those have the potential to adapt seamlessly at an individualized level thanks to Generative AI.
When you couple this highly personalized future with the power of top-line AI – that is the ability to integrate a number of offerings into a single customer experience from trip planning to healthcare to wealth management – you can start to see why incumbents have yet another opportunity to win. Vanguard’s AI-powered robo-advisors offer personalized investment strategies to cater to diverse customer needs. These can only feel personalized where there is enough optionality to even offer in the first place. The more an organization has to offer via their own products or services or even via that of their global partnerships (another arena large companies tend to excel), the more they benefit by being a single destination for customers. Where before rigid brand architectures and “product search” challenges could result in customer confusion, generative AI can seamlessly weave all these offerings together to ensure customers get exactly what they want, when they want, and importantly, how they want it.
Advantage 3
Pricing power
drives long-term returns
Finally, one topic that is far too often ignored as it relates to the forthcoming AI revolution is the role of pricing. In previous eras, especially our venture capital driven recent past, young companies had incredible resources before they had even proven themselves, and new competitors could afford to undercut on price while incumbents dealing with margin demands struggled to maintain market share. In a world of personalized commerce and experiences, the size of a company can much more effectively drive its pricing power. The broader the ecosystem of offerings and the clearer the view into life-time customer value (viewable with the right data), the more flexible a market leader can be in what it charges, when, and to whom.
This applies beyond just promotional cycles. Dynamic pricing is on the rise and has been a game changer for insurers and mobility companies, but with AI the barriers to test and learn down to very specific audiences is unprecedented. Uber has leveraged dynamic pricing for years because it has a clear view of supply and demand. Now imagine how that evolves in any category for companies that know exactly how much they can sell and over what time period to new or existing customers. As noted above, startups are often single product companies (something Instagram has been a uniquely prolific creator of), while large companies with portfolios of offerings that are diversified across goods and services have many more variables they can leverage to make real-time pricing decisions to maximize lifetime value, not just secure immediate transactions.
Companies with diverse offerings can view and respond to each customer in a wholly personalized way that looks at the bigger picture of the value they bring.
Of course, that doesn’t mean large incumbents won’t continue to feel pricing pressure from upstarts, but it does mean they will have more control than ever on which customers they want to invest in, and which may actually be best left to attrition.
Accelerating this change is the fact that traditional loyalty programs are giving way to what could arguably be called membership models. This trend will only dramatically increase as AI becomes more commonplace in pricing decisions. Companies with diverse offerings can view and respond to each customer in a wholly personalized way that looks at the bigger picture of the value they bring depending on their understanding of how consumers access various parts of their portfolios, and those that are functionally integrated (vs. horizontally or vertically) will have unlocked a new superpower via AI. Where The Innovator’s Dilemma is really just the difficulty of prioritizing tomorrow over today for a mature company, generative AI has the potential to evolve relationships with the real-time pricing models to manage for both effectively.
Innovation,
without the garage
We all expect AI to be revolutionary across the economy, and the earliest signs are already here for any organization that has even experimented with the technologies. What’s different about this revolution is that existing data infrastructure, partnerships, and customer relationships are actually the very tools to best lead the revolution.
For decades, multiple technology revolutions (web, mobile, cloud) have scared incumbents eyeing new forms of competition on every side as they read about the “overnight success” stories emerging from nowhere in their respective industry trades. Yet, the future looks starkly different as the AI revolution takes hold: this next era of disruptive technologies will actually benefit those that are already large and diversified. It’s a revolution where innovation will come less often from a garage, and instead emerge from the board room.
...At least for those companies that understand the unique advantages they have if they dare apply it.
Superformats
Will
Save Media
in the
New Internet
Overheard at ON_Discourse
Editor’s note: A new model for media is taking shape right in front of our eyes. What does it look like? That’s where it gets a little complicated. As you can see in this post, the ongoing disruption is real but the way it ultimately shakes out cannot be accurately predicted. Nevertheless, this post lays out a compelling case that media will be revitalized in 2025. Stay tuned for more follow ups on this front.
This post was written by a human and narrated by an AI-generated voice. (powered by Wondercraft.ai).
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The ad-sponsored media model is dying. Good riddance. This system propagated the worst traits of an inefficient (and dumb) economic model: placing annoying ads on top of unnecessary articles to chase after search traffic. It is hard to imagine a more self-destructive system. A new model is emerging, but the specifics of it are not yet clear.
Substack Started It
Substack sparked a half-step towards a new model. It demonstrated that individual creator voices delivering quality content can drive revenue, but not necessarily scale. The key building block of this success was the direct relationship between creators and audiences. The surge of newsletter subscriptions hinted at a long festering demand.
Content alone will not sustain subscription revenue, nor can it really define the underlying relationship.
The key problem to overcome, however, was the content. Content alone will not sustain subscription revenue, nor can it really define the underlying relationship. This novel system was a start in search of a next step. Consumers need more direct value and all these creators could offer was another email.
LLMs Will Shape It
The next step came with the rise of LLMs and other generative systems. These systems convert bytes to bits and repackage them into customized, personalized experiences. Now the creator/audience relationship has a jolt of relevance. The traditional content formats – the articles, emails, podcasts, etc – that contain all of the context and information are now able to be collected, recalled, and repurposed to fulfill multiple consumer needs. On top of that, the delivery mechanism is now centered around context-rich moments. Rather than passively receiving emails, these systems respond to consumer prompts.
Introducing Superformats
I am calling this real-time development of personalized content packages the “superformat.” This system can translate and execute differing consumer signals into a more direct value-exchange. Consumer signals that hint at buying can be met with CTAs to purchase (with affiliate deals). Signals to learn more can drive to lean back consumption (with relevant sponsorships). And following developing stories can result in predictable consumption habits that are valuable for brands.
If the traditional article page is a glass, the superformat is the water in it. Internet 2025 is when the glass breaks and the water spills out.
One thing you will notice about a superformat is that it is deliberately amorphous. It is the anti-snowfall, in the sense that its shape and production are determined by the context of the user, not the requirements of the advertiser, publisher, or even the product designer. It is easier to understand the essence of a superformat than it is to imagine what one looks like. If the traditional article page is a glass, the superformat is the water in it. Internet 2025 is when the glass breaks and the water spills out.
A New Kind of Media Model
Once you accept the atomization of content, the rest is easier to follow. The old order that held things together in silos can no longer hold. My vision for this new system is still taking shape so I can only paint a generalized picture: individual creator voices will partner and merge their audiences in decentralized touchpoints. They will organize more around psychographic traits than topic verticals or geographic locations. The makeup of these businesses will look different than conventional publications, but stories and storytelling will still be the essential lubricant that keeps the value flowing for all parties.
Once you accept the atomization of content, the rest is easier to follow.
The change that is coming is going to be big, but that does not call for doomerism or pessimism. There is a habit to over-interpret sensational stories about AI disrupting industries and jobs. The market is still open to those that are willing to adapt and to more closely connect with the needs of their audiences. Throw away the conventions and just deliver the goods.
