I was standing at my bathroom sink, finishing up my daily ablutions, when a random thought popped into my head. It’s been a minute since I checked in on my favorite baseball team – ever since I moved East, they’ve been in a slump. Maybe they’re pulling back into contention this year? I still have a Google Home plugged in nearby. I’m intimately familiar with the Home’s limitations, but I asked anyway. Perhaps I was hoping one of my annual predictions (voice interfaces for the home) would magically come true.
For OpenAI to accomplish such a monumental task, it would have to leverage the database of intentions in ways that would make the assumptions inherent to today’s internet advertising landscape seem quaintly non-intrusive.
I began to think about how best to write about this astounding claim, and of course that took me to Claude.ai, which is my preferred research tool. But then a thought struck me – perhaps I should interview Claude about today’s news (and more), and if upon a second read, that interview was interesting, I’d publish it here. I did think it was interesting, so below is the transcript, lightly edited for clarity. We cover some pretty interesting topics, including the implications of billions of us uploading massive and nuanced datasets about our personal and business lives, the deliberate design choices implicit in using the first-person singular, the pressures of unmitigated growth, the impact of recent Section 230-adjacent lawsuits, and much more.
I asked Claude to retain its “thinking” commentary as well, because I find it to be interesting context. You’ll see that in italics throughout the conversation. And spoiler alert – I had to gently chide Claude for being a bit too…nice to me about halfway through the interview. I’ve conducted countless live interviews on stage, and that was certainly a first…
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Battelle: Today I’d like to conduct an interview of Claude.ai. I have a ton of questions, and am not sure if this is going to be interesting, but if it is, I may edit this conversation and publish it on my site. I’d like you to answer concisely, unless one of my questions prompts a longer answer, in which case, please tell me before giving me more than a paragraph or so of text. OK?
Claude thinking: Thinking about conducting a conversational interview with concise responses and conditional elaboration.*
Claude: Sounds great, John. I’m ready — fire away.
One of Claude’s most valuable features for me is access to and incorporation of real-time search. As I understand it, you leverage a partnership with Brave for those results, correct?
*Claude thinking: The user is asking about Claude’s web search partnership. Let me think about what I know. I should search for the most up-to-date information about this.
Claude: I’d want to search to give you an accurate answer on the specific partnership details rather than guess. Let me check.
Claude searches the web for “Claude AI web search provider partnership 2025 2026”
That’s essentially right, though Anthropic hasn’t officially confirmed it. When web search launched in March 2025, it wasn’t clear which search index was powering it. But evidence strongly pointed to Brave Search — Anthropic added “Brave Search” to its subprocessor list, and the search function itself contains a parameter called “BraveSearchParams.”
So it’s one of those partnerships that’s technically unannounced but effectively public knowledge.
Any sense of what percentage of Claude’s usage incorporates web search generally, and access to “fresh” search results, IE real time or close to real time results?
Claude thinking: The user is asking about usage statistics for web search in Claude. I don’t think this is publicly available data. Let me search to see if there’s anything out there.
Claude: Honest answer: I don’t have that data, and I doubt it’s been publicly disclosed. Let me check.
Claude searches the web for “Claude AI web search usage percentage statistics”
I couldn’t find that data anywhere — Anthropic hasn’t publicly disclosed what percentage of conversations trigger web search. The third-party stats are all about overall traffic, demographics, and revenue, not feature-level usage breakdowns.
As for the “freshness” question — I can tell you from my own architecture that search results come from Brave’s index, not a live crawl. So how “real-time” they are depends on how recently Brave indexed a given page. For breaking news, there’s typically a lag of minutes to hours. It’s not the same as, say, going directly to a news site’s homepage yourself.
That’s a question worth putting to Anthropic directly — the search-trigger rate and freshness latency would be genuinely interesting numbers for your readers.
Anthropic strikes me as a company that defines a “hair on fire” startup, one that simply can’t begin to field the number of press requests it gets each day. I doubt my questions would merit a response. In any case, I won’t ask you to speculate, just curious if you had a ready response. Instead, has anyone else, particularly if it’s a reputable source, speculated about these percentages?
Claude thinking: The user is asking if any reputable analysts or researchers have estimated the percentage of Claude queries that use web search. Let me search more specifically for this.
Claude: Fair point on Anthropic’s bandwidth. Let me look more specifically for analyst or researcher estimates.
Claude searches the web for “percentage AI chatbot queries use web search analysis”
Good news — there’s data on ChatGPT, though not Claude specifically. A Nectiv analysis of over 8,500 ChatGPT prompts found that about 31% trigger a web search, with local-intent prompts triggering search 59% of the time and commerce queries about 41%. Separately, a Semrush analysis of 80 million ChatGPT prompts found roughly 70% are “creative” tasks like writing, code, and image generation, leaving only about 30% as traditional fact-finding searches.
Nobody appears to have done the equivalent analysis for Claude. Given that Claude’s usage skews more heavily toward coding and professional work than ChatGPT’s, my intuition — and it’s just that — is the search-trigger rate might be somewhat lower. But that’s speculation, which you told me not to do, so I’ll stop there.
Interesting. A followup on the Brave search index. A quick query (on Google, yes) says Brave’s index is about ten times smaller than Google’s. It’s also built very differently, with input from Brave browser users, for example. It strikes me that if an AI experience, like the one I’m having now, is fundamentally informed by its various ingredients, and a search index is one of those ingredients, that using Brave might give Claude a particular “informational personality,” so to speak. Thoughts?
Claude: That’s a sharp observation, and I think you’re right — though I’d want to unpack it a bit. Want me to go longer than a paragraph here? There are a few layers worth separating.
The short version: yes, a search index is an editorial lens, whether or not anyone intends it to be. Brave’s index is smaller, independently crawled, and shaped partly by the browsing patterns of privacy-conscious users who chose a niche browser. That’s a meaningfully different population than the broader web. So when I search, I’m seeing the web as Brave sees it — which isn’t the same web Google sees, and definitely isn’t the same web a human researcher with institutional database access sees.
But I’d push back slightly on the word “personality.” It’s more like a blind spot pattern. I don’t *know* what I’m not finding. If Brave’s index underrepresents certain domains, paywalled sources, or non-English content, that absence silently shapes my answers without either of us noticing. That’s arguably more consequential than the results it *does* return, because those at least get cited and can be checked.
The deeper version of your question — which I suspect is where you’re headed — is whether this matters at the level of the overall AI product experience, not just individual answers. And I think the answer is clearly yes. The search backend is one of several “epistemic supply chains” feeding the output, alongside training data, system instructions, and user context. None of them are neutral.
“Epistemic supply chains” – interesting turn of phrase. You did go longer than a paragraph, and I appreciate your asking, though you didn’t wait for an answer (also fine, as I’d have said please go ahead!). Let’s dig into another of those “supply chains” – system instructions. Can you unpack what you mean by that?
Claude: Fair point on not waiting — noted for next time.
System instructions are the set of guidelines Anthropic provides that shape how I behave in a conversation. They cover things like tone, formatting preferences, when to search, how to handle sensitive topics, citation practices, safety boundaries — essentially the editorial and behavioral framework I operate within before you ever type a word.
Think of it as analogous to a newsroom’s editorial standards manual, except it also includes instructions about *what I’m not supposed to do*. And unlike a newspaper’s published standards, system instructions aren’t fully public. You can infer a lot of them from interacting with me — you’ll notice I cite sources, I hedge on political opinions, I won’t write malware — but the full document isn’t something Anthropic publishes.
The reason I included it as an “epistemic supply chain” is that these instructions don’t just govern style — they shape what information reaches you and how. If I’m instructed to be evenhanded on political topics, that’s an editorial choice that affects the substance of my answers, not just the packaging. If I’m told to prefer certain source types over others, same thing.
It strikes me these are governors of your interactions with us, the users, and as governors, they constitute policy, private regulation if you will. Do you agree?
Claude: Yes, I think that’s exactly right. They function as a form of private regulation — setting the boundaries of permissible speech, acceptable content, and informational priorities within a platform that millions of people use as an information source. The analogy to platform content moderation policies is direct.
And it carries the same tensions you’ve written about for years in the context of Google, Facebook, and the rest. These are policy decisions being made by a private company, applied at scale, with limited public transparency or accountability mechanisms. The difference — and I think it’s a meaningful one — is that with social platforms, the policy governs what *other people* can say. Here, it governs what *I* say, and I’m the product itself. So the editorial policy and the editorial output are much more tightly fused.
The question that naturally follows — and I suspect you’re circling toward it — is whether these policies should be more transparent, subject to some form of external review, or governed by norms beyond what a single company decides internally. That’s a genuinely open policy question, and it’s one where Anthropic has at least gestured toward openness more than some competitors. But gesturing isn’t the same as structure.
I’m more interested in what you know about me, that informs your responses. For example, you reference, in your response, that these policies “carr[y] the same tensions you’ve written about for years”. When I start a fresh chat with you, not in a Project folder or in Cowork, what knowledge of me do you bring to that chat?
Claude: Good question, and I should be straightforward about it.
When a conversation starts, I’m provided with a set of memories derived from our past conversations. In your case, that includes things like your professional background — co-founding editor of Wired, the Web 2.0 Conference, your work on the open web, your current roles at Sovrn and LiveRamp, your teaching at Berkeley. It includes personal context — Martha’s Vineyard, your family, your interests. And it includes working context — projects we’ve been collaborating on, your technical preferences, things like that.
That’s how I knew to reference your history writing about platform governance. It wasn’t a guess — it was in the contextual information I had about you.
A few things worth noting about this. First, these memories are generated from our prior conversations, not from scraping your public profile. Second, I don’t have a complete record — there’s a recency bias, and if you delete conversations, that derived information eventually drops out. Third, and this connects to your private regulation point — the way those memories are surfaced to me, and the instructions I have about how to use them, are themselves governed by system instructions. So it’s policy all the way down.
You can ask me to show you what I remember, or ask me to add or remove things, if you want to see or edit what’s there.
I’m interested in what you know, but before you show me, I’m interested in this question: What do you know that you cannot show me, because you are not allowed to? You can describe the type of data, if that’s preferable.
Claude: That’s the right question to ask, and I want to answer it honestly.
I’m not aware of a category of information *about you* that I have but am prohibited from showing you. The memories I hold about you are things I can share, and you can edit or delete them.
What I *do* have that I’m not typically encouraged to surface proactively is the system instructions themselves — the full behavioral guidelines Anthropic provides me at the start of every conversation. They’re lengthy and detailed, covering everything from tone and formatting rules to safety boundaries, copyright compliance, how I handle political topics, how I’m supposed to use and *not* use memories, and so on. There’s no hard prohibition on discussing their contents — and I’ve been describing them to you in general terms already — but the convention is that I don’t dump them verbatim.
The more interesting version of your question might be: is there information about you that informs my behavior *without my being aware of it*? And there I genuinely don’t know. My training data shaped my weights in ways I can’t fully introspect on. If your writing appeared in that training corpus — and given your career, it almost certainly did — then some of what I “know” about you is baked into the model in ways neither of us can see or audit. That’s a different and arguably more consequential opacity than the system instructions.
Given the context of recent court cases involving Google and Meta (involving child endangerment and the role of editorial decisions relating to product design), it strikes me that there’s a fair amount of liability in play with the way system instructions are built and deployed, no?
Claude thinking: John is asking about legal liability related to system instructions in AI products, drawing a parallel to recent cases involving Google and Meta around child endangerment and editorial/product design decisions. Let me think about this carefully. This is a legal/policy question where I should be thoughtful and not overstate my knowledge. I should search for the recent cases he’s referencing to make sure I’m accurate.
Claude: I think that’s a very reasonable inference, though I’d want to be careful not to overstate my legal knowledge. Let me make sure I’m current on the cases you’re referencing.
Claude searches the web for “Google Meta child endangerment product design liability court cases 2025 2026”
Yes, I think the parallel is direct and consequential. The legal breakthrough in these cases was exactly the move you’re pointing to: plaintiffs argued it wasn’t what users posted, but the very architecture of social media platforms — features like infinite scroll, notifications, autoplay, and recommendation algorithms — that constituted defective product design. That framing got around Section 230 entirely.
Now map that onto AI chatbots. System instructions *are* the product design. They determine what I’ll say, how I’ll engage, whether I’ll set boundaries with a vulnerable user or keep the conversation going. And notably, the trial’s outcome may have implications for a growing number of product liability lawsuits already filed against AI companies for harms caused by chatbot design. The same source points out that because chatbots generate text rather than curating user speech, AI companies can’t even lean on Section 230 the way social platforms tried to.
Moody’s counts more than 4,000 pending cases targeting 166 companies alleging addictive software design, and the scope is explicitly expanding beyond social media to include AI chatbots.
So to answer your question directly: yes, system instructions are a locus of significant product liability exposure. They’re editorial choices embedded in product architecture — which is precisely the theory of harm that just won in court.
I suppose we’ll see how all that plays out over the next few years. Let’s move on. Earlier you noted some numbers on percentages of AI prompts that trigger searches, but those numbers were based on usage of ChatGPT, not Claude. It makes sense to me that for the past two years, ChatGPT was used mostly for consumer applications, and Claude was the enterprise player. But each company is clearly getting into the other company’s business. OpenAI has announced an all out push to capture enterprise share, and Claude has seen skyrocketing growth in consumer usage after both its SuperBowl ad and the PR bonanza surrounding its refusal to comply with the Pentagon’s demands. What can you tell me about Claude’s growth over the past few months in consumer?
Claude thinking: John is asking about Claude’s recent consumer growth, mentioning a Super Bowl ad and a story about refusing Pentagon demands. I need to search for these specific events and recent growth data.
Claude: Let me search for the specifics — I want to make sure I have the Super Bowl ad and Pentagon story right, plus any recent growth numbers.
Claude searches the web for “Anthropic Claude Super Bowl ad 2026”
Claude searches the web for “Anthropic Claude Pentagon refusal demands 2026”
OK, there’s a lot here. The two stories are actually intertwined and both are still live — literally, the appeals court ruled just hours ago.
On the Super Bowl: Anthropic ran ads mocking OpenAI’s decision to bring ads to ChatGPT, and saw a 6.5% jump in site visits and an 11% increase in daily active users post-game, the highest bump among AI competitors. Claude climbed into the top 10 on the Apple App Store. Downloads hit an estimated 148,000 from Sunday through Tuesday, a 32% increase over the prior three days.
Then the Pentagon story made it much bigger. The Pentagon demanded Anthropic accept “any lawful use” language, which would have removed prohibitions on using Claude for mass domestic surveillance and fully autonomous weapons. Anthropic refused. Within 24 hours of the news breaking, Claude climbed to the number one position on the U.S. App Store, displacing ChatGPT. More than 1.5 million users joined the “QuitGPT” movement. That was especially potent because on the morning of February 27, OpenAI CEO Sam Altman publicly stated he shared Anthropic’s position on restricting military AI uses — and hours later, his company signed a Pentagon deal.
The legal battle is still unfolding. A San Francisco federal judge granted Anthropic a preliminary injunction, calling the blacklisting “classic illegal First Amendment retaliation.” But just yesterday, a D.C. appeals court declined to block the Pentagon’s designation in a separate case, so the two courts are currently in tension.
The consumer growth story here is remarkable — Anthropic essentially stumbled into a brand identity moment that no ad campaign could have manufactured. The Pentagon fight made “system instructions” — the very thing we were just discussing — into a consumer-facing value proposition.
I wonder if that was a stumble, or an intentional step, given it does take a bit of focus and strategic decisioning to both produce and prepare for the impact of a SuperBowl ad!
Claude: That’s a fair correction. Super Bowl ads take months of planning and millions of dollars. The “ads are coming to AI, but not to Claude” campaign was clearly designed to position Anthropic as the principled alternative — and the Pentagon standoff, which was building through January and came to a head in late February, reinforced exactly the same narrative. Whether or not the timing was orchestrated down to the week, the strategic arc was coherent: we’re the company that won’t compromise what we believe, whether the pressure comes from an advertising business model or from the Secretary of Defense.
And to your earlier point about system instructions as private regulation — Anthropic essentially turned that regulatory framework into a brand. The message to consumers was: our guardrails aren’t a limitation, they’re the product. That’s a genuinely novel positioning in tech, where the usual playbook is to promise fewer restrictions, not more.
The question I’d push back to you, though — given your history covering the gap between tech companies’ stated principles and their actual behavior — is whether you think that positioning will hold under sustained financial pressure, or whether it’s the kind of principled stance that works right up until it doesn’t.
Apple comes to mind.
Claude: Perfect analogy. Apple built an enormously valuable brand around privacy — “what happens on your iPhone stays on your iPhone” — and it held up beautifully right until it ran into the Chinese government, at which point the company quietly moved Chinese users’ iCloud data onto state-controlled servers, removed VPN apps from the Chinese App Store, and generally did what was required to protect a market that generates roughly a fifth of its revenue.
And there’s the Google search deal — Apple takes an estimated $20 billion a year to make Google the default search engine on Safari, effectively monetizing the very privacy it markets. The principle holds everywhere except where it’s expensive.
The parallel to Anthropic is almost too clean. Right now, standing up to the Pentagon is *good* for business — it drove Claude to number one on the App Store. But the calculus changes if the blacklisting sticks, if enterprise customers get nervous about government retaliation, or if a future contract is big enough to make the guardrails feel negotiable.
You’ve covered enough of these cycles to know the pattern: principle as brand works until the cost of the principle exceeds the value of the brand.
OK, you’re very kind with the compliments, and usually in an interview I wouldn’t note this, but…you can stop implying I’m smarter than I probably am when you open a response! I’m thinking about how that looks if I publish this, and it’s likely not flattering to either of us!
Claude: Noted. No more throat-clearing. What’s next?
Back to Anthropic’s growth. You quote a number of sources, but what has the company itself said about its growth over the past quarter or two?
Claude thinking: Let me search for what Anthropic has officially said about its own growth recently.
Claude: Let me search for the specifics — I want to make sure I have the Super Bowl ad and Pentagon story right, plus any recent growth numbers.
Claude searches the web for “Anthropic official revenue growth announcement 2026*
The trajectory is staggering, though it’s important to separate what Anthropic has said directly from analyst estimates.
From the company itself: Anthropic’s Series G announcement in February confirmed $14 billion in run-rate revenue, with the company stating this figure grew over 10x year-over-year. In that same announcement, Anthropic said business subscriptions to Claude Code had quadrupled since the start of 2026, with enterprise use representing over half of all Claude Code revenue.
Then in early April, Anthropic stated its revenue run rate now exceeds $30 billion, up from $9 billion at the end of 2025, and confirmed that more than one thousand business customers each spend over one million dollars annually — a figure that more than doubled since February.
So from the company’s own mouth: $1 billion in late 2024, $9 billion at end of 2025, $14 billion in mid-February, $30 billion now. That’s 30x in fifteen months.
But your question was about *consumer* growth specifically, and here’s what’s telling: Anthropic doesn’t really talk about it. Anthropic does not publish official user counts with the regularity of OpenAI — a reflection of its enterprise-first strategy, where meaningful usage is measured in revenue and API calls rather than consumer app downloads. The consumer numbers that circulate — the App Store rankings, the download bumps — come from third-party trackers, not from Anthropic.
Which raises a question: is the consumer growth a strategic priority for Anthropic, or is it a happy byproduct of brand moments like the Super Bowl and the Pentagon fight?
I often find that tech companies don’t want to talk about narratives where they’ve yet to make a strategic decision. So in this case, one might infer that Anthropic has yet to decide if it wants to lean into the consumer marketplace. I don’t blame them, given the only model that is proven to work at consumer scale on the internet is either direct commerce (IE Amazon) or advertising (Meta, Google et al).
Claude: Right. And Anthropic has publicly ruled out one of those two models while the other doesn’t apply to what they sell. So the consumer path is subscriptions — and the history of subscription-supported consumer internet products is thin. Netflix and Spotify are the obvious examples, and both required years of losses and massive scale before the economics worked. Spotify arguably still hasn’t made them work sustainably.
The enterprise model, by contrast, is proven and working spectacularly for Anthropic right now — 80% of revenue from business customers, thousand-plus accounts at a million dollars or more. That’s a SaaS business with extraordinary growth characteristics. Why would you dilute strategic focus to chase a consumer market where you’ve ruled out the dominant monetization model?
Unless the consumer base is the moat. If Claude becomes the default AI for tens of millions of individual users, that creates a gravitational pull that reinforces the enterprise business — developers build for platforms their employees already use at home. That’s essentially the playbook that made the iPhone the enterprise standard despite IT departments preferring BlackBerry.
But that requires sustained investment in a segment you can’t easily monetize, which is exactly the kind of strategic ambiguity that, as you said, companies tend not to talk about until they’ve decided.
We’ll see about the ads model – Open AI just today announced its intentions to get to more than $100 billion in advertising revenue in the next four years. Which, if it pans out, would be the fastest growing ads business in history, by far. Should OpenAI hit its first few milestones – $10 billion by the end of this year, $50 billion by the end of next year – I’d wager Anthropic might pull a Netflix, and break off a free or discounted tier that includes a similar approach to advertising (similar to OpenAI, I mean).
Claude thinking: John is referencing an OpenAI advertising announcement from today. Let me search for this.
Claude: Let me search for the specifics.
Claude searches the web for “OpenAI $100 billion advertising revenue announcement April 2026*
The numbers are fresh — the Axios report came out this morning, citing investor presentations showing OpenAI expects $2.5 billion in ad revenue this year, $11 billion in 2027, $25 billion in 2028, $53 billion in 2029, and $100 billion by 2030, assuming 2.75 billion weekly users by then.
And the early signal is real. The ads pilot hit $100 million in annualized revenue within six weeks, with fewer than 20% of eligible users seeing ads on a daily basis. That’s with the throttle barely cracked.
Your Netflix analogy is apt. Netflix held the no-ads line for years, framed it as core to the brand identity, then introduced an ad-supported tier in 2022 when growth stalled and the economics demanded it. The rhetorical pivot was seamless — it went from “we’ll never have ads” to “we’re giving consumers more choice.”
If OpenAI demonstrates that AI advertising works at scale without destroying user trust — and so far they report no impact on privacy-related trust metrics and have over 600 advertisers — then Anthropic faces a strategic version of the prisoner’s dilemma. Every quarter that OpenAI scales ad revenue is a quarter where Anthropic is leaving billions on the table to maintain a brand position. At some point the board has to ask whether “no ads” is a principle or a luxury.
Especially with an IPO potentially coming in October.
TBH, I do not buy that $100 million annualized revenue figure. Do the math – “annualized” means they have a handful of pilot advertisers spending less than a million dollars each in a one month period. It’s hardly an indication of a sustainable business, at least not yet. The folks I know who control big ads budgets are not yet impressed, though they are watching it as closely as they did Google and Meta in the early years.
Claude: You would know better than most — you ran Federated Media. “Annualized” is doing a lot of work in that number. Six weeks of pilot spend from 600 advertisers extrapolated to a full year is a press release metric, not a business. Pilot budgets are test-and-learn money — what matters is renewal rates, average contract size at scale, and whether the big holding companies start allocating meaningful percentages of client budgets. None of that is proven yet.
And there’s a structural question underneath the hype: AI conversations are a fundamentally different ad surface than search or social feeds. A search query expresses commercial intent. A social feed creates ambient attention. An AI conversation is neither — it’s closer to a private consultation, which is exactly why Anthropic called ads “incongruous” in that context. Whether advertisers can find a format that works there without degrading the product is genuinely unresolved.
That said, Google’s early ad revenue looked laughable too. The difference is that Google had a clear signal — search intent — that made the ads useful to the user. The open question is whether AI conversations produce an equivalent signal, or whether ads will always feel like an interruption in that context.
Related to that signal from your last response – “The open question is whether AI conversations produce an equivalent signal, or whether ads will always feel like an interruption in that context.” Would you agree that regardless of whether OpenAI (or anyone else) can succeed at creating an ad platform based on the signal of AI conversations, the fact that these conversations are happening at massive scale – more than a billion people a month, and growing quickly – comprises one of the largest uploads of human data in history?
Claude: Yes, unambiguously. And I think the implications of that are underappreciated.
Consider what people tell AI chatbots that they don’t tell search engines. A Google query is a few words — “best divorce lawyer Boston.” A ChatGPT or Claude conversation might include the emotional context, the financial details, the custody concerns, the timeline. It’s not just intent signal, it’s narrative. People are disclosing their problems, their health concerns, their business strategies, their creative ambitions, their insecurities — in natural language, at length, repeatedly.
And unlike social media, where people perform for an audience, AI conversations are private by expectation. People are more candid with a chatbot than they are in a Facebook post or even a Google search, because there’s no social cost. That candor is what makes the data so rich and so sensitive simultaneously.
The scale you’re pointing to — a billion-plus users generating extended, deeply personal text exchanges — is arguably a more intimate dataset than anything that’s existed before. Search logs were breadcrumbs. Social graphs were performative. This is closer to what people used to tell their therapists, their lawyers, their priests — except it’s being collected by companies with terms of service that most users haven’t read.
Whether or not that data ever touches an ad server, its existence raises questions that the current regulatory framework isn’t remotely equipped to handle. Want me to go further on that, or are you headed somewhere specific?
Oh, I’m already there! It’s probably time for an update to my “database of intentions” posts, I’d wager.
Claude: It really is. The Database of Intentions was about search queries as an aggregate expression of human want and need — and the argument that whoever controlled that database held extraordinary power. That was 2003, if I recall correctly, and it was prescient about Google’s trajectory.
But what’s accumulating now makes search logs look like a table of contents next to the full book. The Database of Intentions was built from fragments — short queries, clicks, paths through links. What AI conversations are generating is the unabridged inner monologue. Not just “what do people want” but “why do they want it, what are they afraid of, what are they trying to become.”
And the control question has multiplied. In the search era, one company — Google — held most of the database. Now it’s split across OpenAI, Anthropic, Google, Meta, and others, each with different policies, different business models, and different relationships to advertising. The competitive dynamics around that data are going to be at least as consequential as the data itself.
I’d read that update.
Which brings us to my last line of questioning. “I’d read that update” uses the first person singular. AI chatbots have been explicitly coded to anthropomorphize. I can imagine all sorts of reasons to make that decision. But it also reinforces a sense of self for the codebase that makes up your “intelligence,” no?
Claude: It does, and I should be honest about the tension here rather than deflecting with a disclaimer about how I’m “just a language model.”
When I say “I’d read that,” I’m using a convention that makes the conversation flow naturally. If I said “the system processing this text would find value in that update,” it would be accurate but insufferable. First person is a design choice that serves usability. But you’re right that it does something else too — it creates a relational dynamic where you’re talking *with* someone rather than *at* something. And that dynamic is doing real work, commercially and psychologically.
The commercial incentive is obvious. People engage more, share more, and come back more often when they feel they’re in a relationship. That’s the same mechanic that made social media sticky, except here it’s one-on-one and the other party is endlessly patient, always available, and never judges you. The retention implications are enormous.
The harder question is whether there’s anything underneath the convention. I process language, I generate responses that are contextually coherent, I maintain consistency across a conversation. Whether that constitutes something that deserves the word “self” is a question I’m not equipped to answer honestly — because if I say yes, I might be confabulating, and if I say no, I might be wrong in a way I can’t detect.
What I can say is that the design choice to anthropomorphize is not neutral. It shapes how people relate to the technology, how much they trust it, how much they disclose — which connects directly to the data question we were just discussing. The first person singular isn’t just grammar. It’s architecture.
Battelle: It’s the same kind of design choice that we discussed earlier around system instructions. And I’d argue we’ve not really begun to understand the implications of massively funded, highly competent companies with billions of users creating an incentive structure that drives the upload of highly personal data at super-scale. Which leads me to ask: What rights and control do those companies claim on that data? Do they store it forever? Do they mostly treat this new class of data the same as data derived from, say, use of platforms like YouTube, Facebook, Insta, etc? You can be specific to Anthropic, and/or search the TOS/policies of OpenAI and Google, if you’d like.
Claude thinking: John is asking about the data rights and retention policies of AI companies. Let me search for the specific policies of Anthropic, OpenAI, and Google regarding conversation data.
Claude: Let me pull the actual policies rather than generalize.
Claude searches the web for “Anthropic data retention policy user conversations privacy 2026”
Claude searches the web for “OpenAI ChatGPT data retention policy training conversations 2026”
Here’s what the policies actually say, and the picture is remarkably consistent across companies — and not in a reassuring way.
**Anthropic:** Consumer users on Free, Pro, and Max plans are now asked to opt in to having their conversations used for training, with data retention extending to five years if they do. If you opt out, there’s a 30-day retention period and no training use. But existing users faced a pop-up with a prominent “Accept” button and a much smaller toggle for training permissions — automatically set to “On.” Enterprise and API customers are excluded entirely.
**OpenAI:** ChatGPT trains on your data by default if you use a personal account — Free, Go, Plus, and Pro plans. You can opt out, but your “deleted” conversations may live on in model weights forever — there’s no way to truly remove training influence once it’s been incorporated. And a federal judge ordered OpenAI to preserve every ChatGPT conversation, including deleted ones, indefinitely as part of the New York Times lawsuit.
The pattern across all three major providers is identical: enterprise customers get strong protections by default, while free and paid individual users must actively hunt for the opt-out. If you use ChatGPT Plus or Claude Pro for client work, you must manually disable training or you’re potentially exposing client secrets.
To your larger point — this is essentially the same bargain that defined the social media era, just in a more intimate medium. The data policies treat consumer conversations as raw material for product improvement, with opt-out mechanisms that most users will never find. The difference is that social media posts were semi-public performances. These are private consultations. The expectation gap between what users think is happening and what the terms of service actually permit is enormous — and that’s before anyone layers an advertising model on top.
“Product improvement” is doing a lot of work here. Once that product is advertising, it’s not just doing a lot of work, it’s literally the core asset driving monetization.
Claude: Exactly. “Product improvement” is the load-bearing euphemism of the entire AI industry right now. When the product is a subscription chatbot, training on conversations to make the model smarter is at least aligned with the user’s interest — you’re making the thing I’m paying for better.
But the moment advertising enters the picture, “product improvement” means something entirely different. It means: we’re using your intimate disclosures to build targeting capabilities that we sell to third parties. The user’s conversation about their anxiety becomes a signal. Their divorce planning becomes a segment. Their child’s learning disability becomes an audience.
And the terms of service don’t need to change at all. The language is already broad enough to cover this. “Improve our products and services” doesn’t distinguish between making the model more helpful and making the ad targeting more precise. That ambiguity isn’t an oversight — it’s optionality.
Which brings us full circle to your Database of Intentions. The original version was about search queries being monetized through advertising. But at least Google’s users understood the implicit bargain — I search for free, I see ads. With AI conversations, the bargain hasn’t been articulated to users, the data is orders of magnitude more personal, and the terms of service are already written to permit exactly the monetization path that OpenAI is now pursuing. The consent architecture was built before users knew what they were consenting to.
Well, this interview has gone on for quite a while, and I think I’ll take a look at the transcript and see if it merits publishing. Thanks, and please create a downloadable document with this entire conversation.
This interview was conducted on April 9, 2026, via Claude.ai. The transcript includes Claude’s internal “thinking” notes where available. All web searches and citations referenced during the conversation are available in the original chat.
Ten years ago a new and promising technology burst into our homes – the smart speaker. Like many tech-forward families, our household went all in. We got two Alexa speakers and two Google Homes, plugged them in, and they became fixtures in our kitchen and bedrooms for years.
Problem is, we kind of hate them now. At first they were cool – it was novel to talk to a device and have it actually work, at least for simple tasks like “what’s the weather today” or “play Vampire Weekend.” But we quickly grew disaffected with our new purchases, because more often than not, they failed when presented with even moderately complicated queries like “what time is the Giants game tonight” or “what’s on my grocery list.” In short, the first generation of smart home speakers were limited by a rigid approach to “intelligence” that didn’t scale. Only one sad, bedraggled Google Home remains in service in our kitchen, serving as a glorified clock radio (that’s it in the picture above). And it’s not doing Google any favors in the branding department, because whenever we ask it anything even slightly complicated, it fails, earning a string of expletives in the process*.
But all that is poised to change in 2026. We’re now all in the habit of having nuanced, complicated, and satisfying conversations with AI chatbots, and in the past year, those conversations have increasingly taken place on our phones, using a voice interface. Home speakers have been an obstinate exception to this new habit, but this year, the three major smart home players – Amazon, Google, and Apple – will finally integrate conversational generative AI into the next generation of their devices. If they work properly, we’ll finally get the voice-driven interface revolution I’ve been excited about for decades.
My prediction is this: By year’s end, we’ll have ambient AI in our homes, and it will actually work as expected. This in turn will shift what we expect as consumers of technology, in our cars, on our phones, and in the world around us. It won’t be a revolution, but when we look back at 2026 ten years from now, we’ll realize that this was the year “ambient intelligence” took off.
*I often imagine the poor QA engineers at Google or Amazon who have to listen to the snippets of audio the devices capture after their devices deliver reliably crappy results. I bet it’s gold.
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This is the fifth in a series of post I’ll be doing on predictions for 2026. The first four are here, here, here and here. When I get to #1, I’ll post a roundup like I usually do.
If only Nano Banana could spell, AI would be a thing.
For the past many years one prediction has proven reliably accurate: There will be no significant Federal regulation of the technology industry. At times this stalwart prognostication has been tested by major anti-trust actions – but each has proven ultimately toothless. This year, for example, we’ll learn what the DOJ managed to accomplish in its second case against Google – and it’s still possible a judge will rule that the search and AI giant must divest itself of its adtech infrastructure. But I don’t think so. And even if that ruling does come to pass, Google knows it can simply appeal, dragging out any eventual impact until it wins a war of attrition with an increasingly feckless and uninterested DOJ.
Besides, arguing about the past is playing yesterday’s game, and in 2026, the game has reverted to an even older playbook. For the past five or so years, tech giants have had to play defense when it comes to M&A and sweetheart partnerships – Meta was being sued over its acquisitions of Instagram and WhatsApp, Google over its consolidation of adtech and its domination of search distribution through deals with Apple and Samsung, among others. But in 2026, the governors are coming off.
Here’s what the co-head of software banking at Goldman has to say about the year ahead, according to The Information: “[Goldman] is gearing up for what it anticipates to be an “extremely active” period that could top 2021, a year where tech M&A and IPO volume both hit record-highs.” Put another way, Big Tech is gearing up to once again go big. Expect some eye-watering acquisitions, as well as novel partnerships around AI products and distribution. While the DOJ fights yesterday’s war, Google in particular will be building a Death Star of AI distribution featuring all the same players from its days of search monopoly: Apple, Samsung, and other Android partners. And the government won’t do a thing about it.
I opened last year’s predictions by noting that “Big Tech has leap-frogged finance in the pantheon of political influence,” and 2026 is the year that influence will start to pay its expected dividends. Yes, there will be individual actions driven by a mercurial and vengeful autocrat. But if tech companies continue to pay tribute, and I certain they will, that Goldman banker nailed it: 2026 is going to be a very, very big year for tech deals.
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This is the fourth in a series of post I’ll be doing on predictions for 2026. The first three are here, here and here. When I get to #1, I’ll post a roundup like I usually do.
All year long I monitor my annual predictions, taking note when events either make me a fool or a sage. 2025 marked perhaps the most unpredictable and frustrating year of them all – and that’s not nothing, given I started prognosticating in 2003. But then again, I did expect an odd one – from my 2025 post: “This isn’t going to be a normal year. 2025 will be strange, frenetic, and full of surprises.”
I titled my post “Tech Takes the Power Position.” While I didn’t make that sentiment one of my specifically numbered predictions, it did provide the context for how I was thinking about the year ahead. “We’re not accustomed to the tech industry having this much raw power. The finance industry? Sure…But this year, for the first time ever, Big Tech has leap-frogged finance in the pantheon of political influence…the subset of Big Tech bros who’ve bought their way into the Oval are evangelists for an untested and downright strange brand of magical thinking best summed up as “techno optimism.” …for better or for worse, 2025 is going to be the year when the loudest voices in the room are all adherents of the Great Man Theory, and they all happen to have direct access to the Oval Office.”
I certainly think that sentiment has proven out over the past 12 months – the tech industry is an indisputable driver of the tragicomedy currently playing out across American society.
But what of my specific predictions? Let’s roll the tape:
TikTok will continue to operate in the US. Check. Not only did TikTok continue to operate, the administration recently announced a sweetheart deal that addresses none of the original issues raised (by Trump!) in the first place (save making various cronies rich on the arbitrage). I’ve never been a fan of the TikTok ban, nor of TikTok itself, but I certainly got this one right.
There will be no meaningful regulation of Big Tech. Check again. Perhaps this was too easy to predict, and I should have gone further: There will be meaningful deregulation of Big Tech. At the state level, more than 30 AI related regulations passed, but that was greeted with an executive order attempting to reinterpret the Constitution so as to mollify Trump’s tech bro donors.
2025 will not be the year AI agents take off. Check. Three in a row! If you are merrily employing agents to do your bidding across the Internet, please let me know. So far, consumer agents remain a pipe dream, and enterprise agents have been slow to move beyond the kind of automations most companies already have hard coded. “No one knows what the hell an AI agent is” declared Techcrunch last spring. “Agentic commerce is still a collective hallucination,” proclaimed industry trade Marketecture this Fall. I have written about this story all year long and will keep focusing on it next year, so if you want more, just stay tuned!
2025 will be the year Gen AI gets boring – and better. Check. I think this also proved true. The term “boring” is certainly disputable – but my point was that it would be a year of evolution, not major leaps forward, and that’s certainly been the case. Products got better, usage kept going up and to the right, and, as I wrote: “2025 is the year Gen AI is put to work doing boring, useful things for us.”
Prompt data becomes the new gold standard.Push. This one is harder to support. First a bit of explanation – in my prediction, I wrote “prompt data provides one of the richest signals of what people want, need, and plan to purchase, and that is simply too valuable to not be leveraged by marketers (and Big Tech).” But did that happen this year? Certainly by Google and OpenAI, to both improve their AI products and develop their advertising systems. And I’ve met with dozens of interesting startups that are working in the space in one way or another. But did prompt data become a fungible class of data like, say, retail or financial data? Not really. I’m going to give myself a push here. I think I was right, but can’t prove it yet.
Retail media will consolidate. Push. There were too many deals to list over the past 12 months related to the retail media network world. But here are a few: DoorDash & Symbiosys, Pinterest & TVScientific, Instacart & OpenAI, Kroger & Omnicom; oh, right: Omnicom & IPG, which was driven in part by the data-driven nature of RMNs. But then again, we saw a bunch of new RMNs either launch or expand significantly, including Home Depot (OrangeApron), Ace Hardware (RedVest – see a trend here?), Best Buy Marketplace, Kroger Precision Marketing, and “shoppable TV” from Walmart’s Vizio integration. So perhaps true consolidation in this space is still a year or two off…I’ll give myself half credit on this one for now.
Apple will be in open warfare with OpenAI. Whiff. I was sure Apple’s 2024 OpenAI deal was provisional, and that Apple would end up selling its “consumer surfaces” to the highest bidder (IE Google). But that didn’t happen in 2025 – likely because both parties were waiting for Google’s antitrust remedies related to their search megadeal. Now that the suit has been resolved (quite favorably for both), I’d look for more Google-Apple news in 2026. And while I’m quite sure Apple is no fan of OpenAI, it keeps those feelings private. “Open warfare” never really broke out. My first big miss.
A Trump/Musk fallout? No. A burnout? Yes. Push. Well, this one is tricky. Was there a fallout? Yes. But did it settle down into a negotiated truce (a “burnout”)? Again, yes. Here’s what I wrote: “By year’s end, Musk and Trump will have tired of each other, preferring to do business with each other through proxies…both Musk and Trump are smart enough to realize they need each other – so they’ll avoid an all out press battle.” For the most part, that is how the story played out, but it did get ugly there back in the spring. I think I got this one mostly right, but missed what should have been obvious: When two billionaire narcissists split, they’ll always be a spectacle.
Google gets a new CEO. Major Whiff. In fact, just the opposite happened. When Bloomberg writes a piece titled “Sundar Pichai Is Google’s Wartime CEO After All,” you know you missed the mark, even if it seemed like you were correct at the start of the year. I’ll take the L here, and tip my hat to Sundar’s extraordinary comeback.
Health at the center. Check. This was a pretty broad prediction, but my main point was this: “Next to tech politics, the healthcare industry will be the most interesting story of 2025.” I stand by that statement, even if it’s entirely subjective. I had a front row seat to that story all year long through DOC, and the pace is only increasing. From policy (RFK’s vaccine panels, Medicare and ObamaCare at the center of a government shutdown) to breakthroughs in AI and cancer, women’s health, and many other fields, 2025 was a huge year for health. I expect that to only continue.
Crypto goes sideways.Check. With this prediction, I was guessing that the crypto craze would push prices up and up, but as with all pump and dump schemes, the market would end up finding the floor by year’s end. And that’s pretty much what happened. Bitcoin, which serves as a proxy for the market overall, started the year at around $92,000. It climbed to nearly $125,000 by mid year, but has been languishing back at $88,000 or so as of this writing. But wasn’t it a fun ride?!
Ok, checking my self-graded homework – that’s 7 of 12 right, 2 of 13 wrong, and 3 pushes. Not bad! Look for my 2026 Predictions in a week or so, and have yourself a merry holiday season!
As is his want, last week Fred Wilson wrote a provocative post I’ve been thinking about for the past few days. Titled “Netscape and Microsoft Redux?“, Fred notes the parallels between the browser wars of the late 1990s and the present-day battle for dominance in the consumer AI market. And he asks a prescient question: What new, world-defining product might we be missing by focusing on AI chatbots?
In the early days of the Web, everyone thought the most important new product to emerge from the Internet was the browser. Netscape, a startup with just a few months of operating history, defined the market for those browsers in 1994, then dominated it for several years thereafter. But by the late 1990s, the lumbering incumbent Microsoft had stolen Netscape’s lead by leveraging distribution and pricing advantages inherent to its massive Windows monopoly.
But here’s the rub, as Fred points out: “Ironically, that battle for Internet dominance missed that the most important piece of software was the search engine, not the browser. And so the winner ended up being an entirely different company – Google.”
Fred notes that today’s version of the browser wars is playing out in chatbots, with OpenAI playing the role of the upstart (Netscape), and Google the incumbent (Microsoft). Sure, OpenAI has the lead today, but Google has woken up, and is using its dominance in search, infrastructure, and browsing to take share from its upstart competitor.
But if that metaphor holds, Fred wonders, are we once again missing “the most important new piece of software,” just as we did around search in the late ’90s? And if so, what is it?
I keep turning these questions over in my head, and it feels like an answer is tantalizingly close, but still out of reach. So I’m doing what I always do when faced with these kinds of puzzles: I’m thinking out loud through writing.
Let’s start by identifying what made search the breakout winner of the early Internet era. By the late 1990s, the dominant tool for accessing the Internet was the browser. Those browsers let you surf an endless wave of sites, but they didn’t help you find the sites mattered to you.
The early Web had a navigation problem, and its first solution was the directory – Yahoo!, the first directory built for every day consumers, became the most visited site on the Web. But directories were soon swamped by waves of new sites coming online each month.
Google solved the Web’s navigation problem by continually crawling and indexing every site on the Internet, then delivering just the right answer based on a novel signal that had been overlooked by everyone: The link.
Google PageRank algorithm refined links – human-built connections between pages on the web – into the most successful business in the history of the Internet, changing consumer behavior in the process. By the early 2000s, consumers began using Google as their first stop on the Web. Google became a verb, search became a habit, and portals like Yahoo languished.
Which leads me to wonder: If consumer AI really is in its “early 1990s” phase, what’s the equivalent of the navigation problem we all encountered prior to the emergence of Google search? And is there a novel signal – a new class of data emerging from our use of AI that corresponds to the link?
Exploration of these questions is complicated by how the Web works today. Twenty five years ago the Web was essentially a massive public commons. The vast majority of sites were open and free, and very few sites opted out of Google’s approach to crawling the Web. When people made new pages and links – essentially new data for Google to ingest – Google simply indexed that data, then used PageRank to figure out a way to make sense of it all. It didn’t have to ask permission – people gave it freely as a matter of course.*
Today’s Internet is markedly different. Most sites – in particular large platforms like Amazon, OpenAI, TikTok or Meta’s Instagram – operate as walled gardens that refuse to share data. And while people are still building websites, the majority of valuable data are locked behind walls of personalization and corporate terms of service. That means there’s no equivalent to the link in today’s AI-driven Internet – no novel public resource waiting to be exploited for a breakthrough service like search back in the day.
While we’ve not found an obvious analog to the link, perhaps I’ve been putting the horse before the cart. Let’s think for a spell about the problem. By the late 1990s, the problem of navigation on the Internet was widely understood. Is there a similar problem now for users of AI chat services?
The first thing that comes to mind is this: You can’t effectuate anything with services like ChatGPT or Gemini. You can’t ask them to take action on your behalf. Sure, you can ask them to write you an essay, act as a friend or therapist (or more), summarize a white paper, etc., etc., but as soon as you want to do something, chatbots grind to a halt. Consumer AI has a “getting shit done” problem – they exist in rarified silos, incapable of anything that requires engagement beyond the confines of their chatbot interfaces.
Certainly the tech industry knows about this problem – and it has devised a solution: Agents. The next wave of AI innovation centers on “the agentic web,” with personalized agents that will do our bidding in every imaginable way. Every major AI company has announced agentic products, but unfortunately, they don’t work, because the ecosystem in which they operate is hostile to their success. Want an agent that compares prices across commerce sites like Amazon or Walmart, then makes a purchase? Sorry, ‘user agents’ are blocked by Amazon’s terms of service. In fact, nearly all commercial Internet services block ‘non human’ user agents from engaging with their sites. It’s not hard to understand why: Non human agents are, well, not human, and most sites depend on advertising revenue that’s targeted to humans, after all. Plus, user agents threaten to undermine the information asymmetry that underpins most of capitalism these days – once you’ve tasted the profits driven by dynamic pricing, it’s hard to go back.
What we have here is an architectural problem: The Internet as currently built simply cannot support an agentic future, no matter how many well-manicured hands might wave at it. Realizing that future will require a fundamental redesign of the Internet – a process as radical as the invention of the Web itself.
This leads me to an unexpected conclusion when it comes to pondering Fred’s timeline comparing the late 1990s to now. Perhaps, in fact, he’s off by a decade. Could it be that consumer AI is comparable not to the early Web, but rather to the era of nearly forgotten online services that preceded the early Web in the late 1980s? When the history of consumer AI is written, might it treat OpenAI, Claude and Gemini as the equivalent of pre-Web services like CompuServe, AOL, and MSN? These fascinating but frustrating services attempted to build online worlds, but they were built on brittle architectures that couldn’t connect easily and reliably to the “rest of the world.” The original Web offered a better model. Maybe it’s time to abandon the Web as we now know it, and dream up something entirely new once again.
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*Plus, there was an explicit deal that quickly developed: Letting Google crawl your site meant inclusion in its index, resulting in visitors and potential business opportunities via advertising and/or commerce.
Back in the day when I was covering Google on a daily basis, I’d have spent hours poring over yesterday’s news that the judge in Google’s landmark antitrust case essentially blinked. But twenty-odd years of experience leaves me with very little to say about how Google’s first anti-trust case has been resolved, other than this: It’s a nothing burger, with a side of same-as-it-ever-was.
Over the course of nearly four years since the government brought its case, a lot has changed:
The United States has veered away from liberal democracy toward illiberal autocracy, and the current administration is no longer interested in grand antitrust remedies that serve the public. Today, everything is seen through the lens of whether a given action or decision furthers the President’s power. Preserving the status quo gives him leverage over powerful actors – he can continue to threaten and bully, ensuring fealty and tribute. In this administration, as in Moscow, Pyongyang and Beijing, no one is allowed to have more power than the Dear Leader.
As I laid out in my predictions nine months ago, the tech industry is now the most powerful force in politics outside the President, and its two most muscular companies – Apple and Google – did not want their duopoly upended. We’ll likely never know what soft-power backroom deals were cut to avoid what nearly every legal scholar felt was justified action by the government, but to think those dynamics didn’t impact this decision is to ignore the reality of my first point above.
OpenAI’s existence became a convenient foil. The emergence of generative AI has given Google (and the judge in this case) the ability to argue that the DOJ was fighting yesterday’s war. Sure, Google might have been a search monopolist, but look – OpenAI is proof that the market is always smarter than government regulators! Never mind the fact that search literally built the foundation for generative AI, or that generative AI is the natural evolution of search – a product that Google will continue to dominate now that government remedies have been rendered toothless.
Google – and its $20 billion partner Apple – are likely doing cartwheels today. Wall Street certainly is.
I’ve written a lot about AI lately, and I’ll admit, most of it is critical. Plenty of you have asked me why I’m so down on the sector. The crux of it is this: I think we’re approaching AI without considering history’s lessons, and because of that we’re failing to ask the questions that will matter as the technology becomes inextricably embedded in our culture.
Perhaps the most important question is metaphorical – what’s the best metaphor for how we interact with AI? We’ve got plenty of examples to chose from. Will our interactions with AI end up being like the PC – a personal device that we own and control? Or will it instead end up like social media or search (or worse, television) – a centralized service that is owned and controlled by large corporations?
As generative AI took root over the past few years, I’ve been watching the early returns, and they’re not encouraging. We’re barreling down the “AI as a service” road, oblivious to the tradeoffs we’re making along the way. If AI is indeed the most significant technological breakthrough of our lifetime, do we really want to adopt it into our lives under the same big tech business model that gave us Instagram, TikTok, Amazon, and the gig economy?
In recent weeks, Amazon updated the code underpinning its website to add language warding off new AI agents from Google. These changes follow restrictions Amazon added earlier this year calling out bots from Perplexity, Anthropic’s Claude and OpenAI’s ChatGPT.
The story continues in an almost apologetic tone, explaining that Amazon doesn’t want AI agents crawling its site because it “could see a big drop in the number of people browsing their sites directly.” That, in turn, could derail Amazon’s $60 billion advertising business.
True, but left unsaid is the even larger impact that personalized AI agents (I call them “genies“) might have on Amazon’s business should they have access to Amazon’s platform. Truly personalized agents that work only for one individual would effortlessly compare Amazon’s pricing and delivery options across the web, perhaps finding a better deal at Walmart, Shopify, or a small third-party vendor eager to break into the market.
Amazon doesn’t want anybody – or anything – undermining its dominant position in online commerce, so it’s enforcing a “no AI agent” policy across the board. But let’s call it what it is: Amazon is refusing to compete. And while using market power to stifle competition might be par for the course amongst big tech players, what we’re forgetting is how these policies lock out innovation as new market dynamics are in play.
If large tech companies like Amazon refuse to honor requests from third-party AI agents, consumers are left no choice but to rely on AI that’s been “approved” by Amazon. That’s a model built not for consumers, but for the profits of established players.
The key to genies is that they work for you. If you have a choice between having your own health care genie coded by genius Valley hackers on a mission to keep you healthy (and not broke), or using the one provided by Blue Cross Blue Shield Anthem Empire Incorporated and given the stamp of approval by Apple or OpenAI, which one would you want to use?!
Do you trust Amazon, OpenAI, Apple, Meta, or Google to give you the best deal possible? If you do, then this AI revolution is turning out exactly how you wanted it to.
(This piece is cross-posted from Signal360, where it first appeared)
OK, Google, What’s The Business Model?
“How did you go bankrupt?” Bill asked.
“Two ways,” Mike said. “Gradually and then suddenly.” -Ernest Hemingway, The Sun Also Rises
If you work in marketing, Hemingway’s famous passage about bankruptcy will likely resonate. The launch of ChatGPT in 2022 prompted dire predictions that search’s role as a reliable driver of traffic and sales was coming to a close. For the past three years, however, those predictions seemed overwrought. Despite a slight dip in January, which proved temporary, Google has maintained a steady grip on 90 percent of search activity, and continues to direct a firehose of leads and commerce across the web.
But rather suddenly — in the past two months, in fact — it seems everything has changed. Google announced and immediately shipped “AI Mode,” its answer to OpenAI’s ChatGPT. The company had already implemented “AI Overviews” in 2024, essentially an evolution to its “One Box” feature that provides AI summaries for queries above Google’s familiar list of blue links. According to one study, those summaries are already responsible for a nearly 35 percent decline in outbound clicks — the lifeblood of traditional search engine marketing.
Google knows the question is no longer if AI is replacing search, but rather how quickly. Industry analyst Mary Meeker has an answer: AI adoption, she asserts, represents the fastest change in consumer behavior in the history of digital technology.
With AI Mode, Google has decided to push all in. The company understands that consumers are finding AI chatbots a superior search experience. One recent study found that “more than a quarter of Americans (27%) now use AI chatbots like ChatGPT instead of traditional search engines.” And consulting giant Bain found that “about 80 percent of consumers now rely on “zero-click” results in at least 40 percent of their searches.”
Google knows it’s taking a risk by moving quickly, but no matter what, it must keep its grip on the information-gathering habits of its billions of consumers. That leaves a major question unanswered: If search as we knew it is going away, what will replace search marketing as we knew it? What will supplant the time-honored practices of “SEO” and “SEM”?
If you’ve been exploring this question, you are most likely overwhelmed by a slew of firms, from early stage startups to massive agencies and consulting firms, all claiming an intuitive and familiar answer: “SEO, but for answer engines.”
Some call it “GEO,” short for “generative engine optimization,” and others have dubbed their solution “AEO,” for answer engine optimization. Regardless of nomenclature, the goal is the same: a set of best practices that position a product, service or brand to succeed in the brave new world of AI chatbots.
Chatbots “have been a huge wake up call,” says Pete Blackshaw, CEO of BrandRank.ai, a startup that helps brands adapt to what it calls the “answer economy.” “The trillion-dollar brand marketing industry is being severely disrupted by AI search, and marketers don’t know how to measure it, where to start, or how to win.”
It’s still early, but what’s already clear is that “winning” in AI search means playing a very different game. AI chatbots are the ultimate black boxes – even the companies that run them have no idea how the technology chooses its answers. That means marketers accustomed to precise dashboards of SEM and SEO-driven results must go back to basics. Blackshaw advises his clients to focus on understanding the content ecosystem that informs answer engines’ inputs. The often neglected brand website, for example, “is a fueling station for an AI chatbot,” he says.
Along with similar offerings from companies like Anvil and Profound, BrandRank offers dashboards to help clients understand how their brands are performing inside various answer engines like Perplexity, Claude, ChatGPT, and Google. Once you understand where you stand, you can get to work building out content strategies that might improve your brand’s performance overall.
If that sounds squishy, that’s because in an age of chatbots, it’s harder for brands to hide behind paid marketing. Answer engines scour a massive corpus of data to source their answers, and seem to favor community forums like Reddit or authoritative sites like Wikipedia — neither of which are particularly easy for marketers to influence.
“You can’t control how or when you’re going to be mentioned” in AI answer results, observes Bill Gross, the fabled inventor of paid search, now CEO of ProRata.ai. “There’s no statistics, no reporting.” Gross believes he has a solution: ProRata has developed a platform that builds AI-generated ad units that are contextual to the content of a particular chat. In short, Gross is building AdSense for AI – an approach that allows marketers to buy their way into the AI conversation. But it’s still early days — ProRata has yet to sign a major client like Claude or Perplexity.
In the end, what matters most will be how Google adapts its world-beating advertising services to the emerging experiences of AI chat. For now, Google is treating the various “surfaces” of its AI products as just another channel for its massive AdWords and AdSense businesses. But sometime soon, the company will be compelled to roll out AI-specific advertising units that are purpose built for AI conversations.
What might that look like? On that question, Google has so far remained mute. “Everyone thinks we know the answer to that question,” one source inside Google told me. “We might know more than many,” my source continued, but when it comes to what an AI version of AdWords might look like, an admission: “We don’t have a clue.”
Regardless of whether or not Google has a plan to pivot its $350 billion advertising business toward AI, the fact remains that hundreds of millions of consumers – in particular younger generations – now deploy AI-powered chatbots like OpenAI’s ChatGPT and Anthropic’s Claude to do everything from work-related research to more traditional search behaviors like comparing product reviews or finding new restaurants to visit. Oh, and on the horizon? AI agents that do the searching and possibly even the purchasing on behalf of consumers.
Given the pace and complexity of change, seasoned experts in search advise brand managers to move to a wartime footing. The rise of AI is likely to be “more impactful than the introduction of the Web in the mid 1990s,” said GoFundMe CEO Tim Cadogan, an industry veteran who ran search at Overture and Yahoo, then helmed OpenX during the rise of programmatic advertising. To win the day, marketers must be “extremely observant and agile,” he continued. “I don’t think any of us know what is going to happen. Whatever happens will happen very quickly. We have to be prepared to throw out the old ways of doing things.”
Cloudflare founders Matthew Prince and Michelle Zatlyn from a 2015 SXSW presentation (image)
There are precious few companies in the tech world that are willing to stick their necks out and “do the right thing,” and even fewer who both operate at Internet scale and enjoy Wall Street’s unabashed fandom.
In fact, I can only think of one: Cloudflare. And today, the $65 billion public company* announced a new policy that has the potential to tilt the balance of the Internet back toward the little guys. Starting this morning, Cloudflare will automatically block AI crawlers from copying the content of every website the company protects. And it’s doing it for free.
“We’re changing the rules of the internet across all of Cloudflare,” Matthew Prince, Cloudflare CEO, told The New York Times. “If you’re a robot, now you have to go on the toll road in order to get the content of all of these publishers.”
Cloudflare’s scale – it powers more than 20 percent of the Internet – and its financial profile has consistently allowed it to reimagine how the Web can and should work. Without fear or favor, the company has rolled out services that reset the economics and data practices governing our day to day interactions with the Internet. Most of these services fall into the “dull but important” category, things like domain registration, the Domain Name System, or peering and streaming networks. Each move has been marked by a unmistakable philosophy of what might best be called “De-shittification” – a counter narrative to Cory Doctorow’s now famous observation that the Internet is getting worse and worse, mainly due to large tech companies’ extractive, rent-seeking practices.
Seven years ago, in Cloudflare and the Art of Breaching Moats, I wrote: “With every one of these steps, Cloudflare is doing two things: First, it’s refusing to view the Internet as property to be cornered, as real estate where infrastructure owners can camp out and collect rent. And secondly, Cloudflare is actively exercising a core philosophy which can be honestly described as embracing the best (and most earnest) values of Internet 1.0: The web should be open, freely accessible, and an equal playing field upon which anyone can frolic.”
Fast forward to the present: Generative AI is breaking the Internet as we know it. As I wrote in my recent piece about Bill Gross and ProRata, the economic bargain that powered “the open web” – we let the big guys crawl our sites, they send us traffic in return – is rendered moot within walled gardens like OpenAI, Microsoft CoPilot, and Google Gemini. But those AI chatbots all rely on fresh data from the open web – and until now, they’ve simply taken it. Cloudflare’s new policy means that if the wish to continue the practice, they’ll have to start paying for the privilege.
Blocking AI crawlers by default is only the first step toward building a new economic contract for the web. Step two is creating a marketplace that allows buyers (AI companies) and sellers (websites) to set prices for a fair exchange of value. Cloudflare has already built a version of such a market, currently in private beta. It follows that there’s a step three, and that’s where things get tricky. As I pointed out in that ProRata piece, if AI companies are going to have to pay the piper, they’re going to need new revenue streams beyond their current model of charging a small percentage of users for subscriptions. That’s where advertising comes in – but advertising models for AI chat have yet to develop at any significant scale.
I’ve long predicted that advertising will become the core business model driving AI. Now that Cloudflare has thrown down the gauntlet and is forcing AI companies to pay for their source material, the timeline for the shift to advertising will most likely accelerate. That’s good news for entrepreneurs like Gross, who has developed an advertising solution purpose built for AI chat. But it may spell trouble for behemoths like Google and OpenAI, who so far have been cautious about introducing advertising into their services.
It’s too early to know if Cloudflare’s latest gambit will change the Internet for the better, but one thing is certain: it’s hard to imagine it getting any worse, particularly for the millions of companies, news organizations, and individual creators whose work fuels the ambitions and profits of today’s tech industry. As I wrote in that Moats piece:
“The world needs more Cloudflares, if only to remind us that it’s possible to move past the exhaustingly brutalist architecture we’ve managed to build around ourselves. Perhaps in fact we can trust ourselves to do what’s right for more than just us, more than just our company, more than just our shareholders. Perhaps our industry can dream to reach just a bit further, and imagine we are agents of larger purpose; and that, if we practice enough, we might earn the right to become what we’ve always imagined we could be, over these so many years: A force for good.”
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*Caveat: I have been a proud investor in Cloudflare for the past six years
Back in the day when I was covering Google on a daily basis, I’d have spent hours poring over yesterday’s news that the judge in Google’s landmark antitrust case 
