I woke this morning to news that OpenAI plans on growing its advertising business from zero to more than $100 billion in the next four years. If that sounds utterly bonkers to you, well, you’re not alone.

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.

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I spend a lot of time engaged in the craft of writing – I’ve penned more than 1.5 million words on Searchblog alone. Writing anchors nearly all my projects, from teaching at universities to my board and investing work, not to mention the hundreds of pieces I’ve either authored or edited at places like P&G Signal and DOC. I write a few pages nearly every day in longhand journal (I’ve filled nearly 30 of them over the past four decades), and I recently embarked on a long-form writing project that may (or may not) produce another book over the coming months.

So writing matters to me, and I’ll admit I’m uncomfortable with how generative AI is changing my chosen field. I recoil from the idea of AI-written articles, blog posts, or academic assignments. And I support the various efforts by authors, journalism institutions, and creative groups who are pushing back against what feels like wholesale theft of our work to train LLMs.

That said, I’m not precious about this craft, despite the fact that it’s been the foundation of my career. I know most people aren’t professional writers, and for them, AI has addressed a very real problem – I may be able to bang out a post like this in less than an hour, but grinding out 750 words of readable text can be an all day chore for someone whose skills lie elsewhere. Plus, there’s a fair amount of writing that is pedestrian in both its purpose and its prose – press releases, short announcements, and summaries of meetings, for example. For those uses cases, AI does a perfectly adequate job of first drafts – as long as a human reviews, edits, and fact checks them, I have no problem with the idea of releasing that work into the world. In fact, over at DOC, we partnered with Anthropic to create AI summaries of many of our sessions from last year’s gathering.

Increasingly writers, organizations, and even publications are working in concert with AI tools to produce finished works, and it’s in this space that I feel our language is failing us.

Journalism has a long established practice of publishing pieces under “the byline” – a sacred concept in our profession. I remember my first byline in the Los Angeles Times, back in 1992. It was both a thrill and a heavy responsibility . That byline meant the Times and I were responsible for everything in the piece – its accuracy, tone, and its effect on the world. Seeing it in print made it real, connecting my work directly to its potential impact. (OK, that first piece was about the police shooting a dog, but still.)

But what do we call a piece that was co-authored with AI? I’m not talking about using AI to help with research or sourcing, but rather a piece that contains writing authored, in part or in whole, by an AI tool? We lack a phrase that contextualizes this kind of writing, so I’d like to suggest a neologism: The withline.

A withline is a formal acknowledgement that the associated work has been co-created with an AI tool. It might take the form of “By John Battelle with Claude AI,” for example. It should carry an understood context of responsibility and human accountability. In brief, a withline would:

• Signal that a work has been co-created using AI tools.
• Indicate that human intelligence has reviewed and validated the claims in the work.
• Connect, ideally, to a policy of AI usage that has been established and transparently communicated by the organization or author conjoined in the withline.

I don’t expect that the withline will become a journalistic standard anytime soon, but we do lack  a term to express the evolving nature of creative work in an era dominated by generative AI. So I propose we consider “the withline” as a way to move forward with a practice that is already well underway.

And no, this piece was not created with AI ;-). But I did ask Claude to come up with a definition, which I posted as the art above.

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What is AI?

I’ve been struggling with this rather basic question for several years now, so today I figured I’d write out loud about it, and see if anything coherent surfaces.

First, let’s define what I mean by “AI.” For the most part, I’m referring to the at-scale generative AI services offered by Google, OpenAI, and Anthropic. But while these three companies dominate the market at present, generative AI is filtering into just about every digital surface where there’s money to be made, and as it does, new leaders will emerge, just as they have in every major phase-change in technology history.

Tech tends to move in a consistent pattern of punctuated equilibrium. A  burst of innovation drives new consumer adoption, which builds into a period of relative stasis as the new products and technologies are integrated into society and the economy. Then a new burst happens, and the pattern repeats.

Sometimes we understand the potential of these waves early, but it takes years for their impact to mature. I’d argue that was the case with the World Wide Web in the early 1990s and mobile in the mid 2000s. Other waves arrive relatively unheralded and take years to develop. The personal computer was first introduced in the 1970s, but it took a decade for it to land on the cover of Time as the “person of the year.” Still other innovations spread quickly and are immediately understood as breakthroughs – it took Microsoft just 18 months to pivot from DOS to Windows once the Macintosh demonstrated the power of the graphical user interface, or GUI.

So where does AI sit in this framework? Clearly its impact was immediately heralded, but we’re still trying to understand what exactly that impact will be. The technology evades easy classification. Is AI a software product or service? A platform, or possibly even an operating system? A utility, like compute or storage? An interface? A new medium?

The easy answer is that AI is arguably all of these things and more. Its ecdysial nature sheds definition like a snake shedding skin. Three years ago we all thought AI was a replacement for search – a product delivered as a service over the Internet. But as the technology evolved and innovators began to build on top of AI’s capabilities, it became clear that AI was more than one product – it was becoming both a utility and a platform. OpenAI announced an app store, Anthropic integrated with critical Internet infrastructure like cloud and hosting providers. Entrepreneurs and VCs pitched AI as a utility that would be built into…everything.

But as billions of us began what is likely a lifelong relationship with AI services, we began to understand that AI represents more than just a product, utility, or a platform – it’s also an entirely new model for how we interact with digital technology, an interface upgrade akin to the shift from DOS to the graphical user interface.

So AI is all these things – a product, a service, a utility, a platform, and an interface. And if AI is all those things, it’s arguably also a new operating system – which the Oxford dictionary defines as “the software that supports a computer’s basic functions, such as scheduling tasks, executing applications, and controlling peripherals.” But with AI, the “computer” is not limited to the machine on our desk or the phone in our hand. My son recently leveraged Claude’s “operating system” to create a novel CRM system for his company (it took him less than a day, sorry-not-sorry Hubspot). It runs in the cloud, is expressed as a web page, and is integrated with at least half a dozen distinct products, including Google Suite, Slack, and SMS. That certainly sounds like what an operating system is supposed to enable – coordinate the underlying capabilities of a computer and providing a platform for its expression. In the case of AI, it’s becoming an operating system for … pretty much everything that might be possible on the Internet.

So is this the metaphor that might stick to AI’s slippery skin? Is AI the new operating system of the Internet? As a hypothesis it feels accurate, but incomplete.

Perhaps AI is also a medium?

The famously inscrutable media theorist Marshall McLuhan defined media as “any extension of ourselves” and “any new technology” that extends our physical or nervous system into the world. That certainly sounds like AI, doesn’t it?

Thanks in large part to McLuhan, digital technologies were collectively dubbed “new media” when they first broke into the popular consciousness in the late 1980s. It was a catch-all phrase ripe with implicit bias: the “old media,” with its one-to-many broadcast model, was giving way to an interactive, electronically distributed model of “new media*.” But the term aged poorly as the web broke out. The web was more than just media, it was a novel platform in its own right. By the mid 2000s, Tim O’Reilly and I, among many others, coined the term “Web as Platform,” which we imagined as open, distributed, and generative in nature. The word “media” simply wasn’t broad enough to encompass the web’s potential.

While much of the web ended up dominated by closed business models of extractive capitalism, I still believe in both the idea of “web as platform” as well as a liberal interpretation of the phrase “new media.” I can imagine how AI might play a role in delivering on the excitement and potential evoked by those now historic phrases. But they are now relics, neither of them ring true to me as potential descriptors of what AI might become in our culture.

So, nearly 1,000 words into this rumination, I’m no closer to answering the question I posed at the top, but there is one word I’ve not yet surfaced – and it builds on the core definition of media: Language.

“Language” is defined as “the principal method of human communication, consisting of words used in a structured and conventional way and conveyed by speech, writing, or gesture.” Language is considered a unique product of human intelligence, and it is our language that provides the foundational corpus for generative AI as we know it today. McLuhan considered language a medium, but then again, he believed the same about the light bulb. Regardless, as I struggle to find a handle by which to pick up the concept of AI, there’s something about “language” that feels appropriate. Maybe AI is, in fact, equivalent to a new language, one we’re just learning to speak. That certainly rings true to me.

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* This was the entire focus of my masters thesis at Berkeley in 1992. Sigh. 

Apple turns 50 years old tomorrow. I’ve been using its products for 48 of those years.

48 years. Over those five decades, my relationship with Apple has shifted as dramatically as its market cap. And not, I am afraid, in a good way.

I first used an Apple product in 1978. I was in middle school when my parents brought home a new Apple II. My mother, a teacher, took advantage of Apple’s focus on the education market. I remember writing papers using huge floppy disks and digging the dot-matrix printer, but that was about it. In the late 70s, Apple seemed like a cool new company at the forefront of a cool new industry. But what did I know, I was a kid.

By the time I left for college, my father had an IBM PC, which I rarely used, and my mother had upgraded to an Apple IIc, which came out at roughly the same time as the Macintosh. I’d taken a few programming courses at my high school – I could write a tic-tac-toe game in BASIC, run DOS programs from the C: prompt, and futz around with some Pascal. But I was a writer at heart, not a coder.

In college I knew enough about computers to cobble together a cloned IBM 286 machine, which I used to earn extra money scripting databases for a small software developer. I couldn’t afford the Mac – it was priced at around $2,500 in 1984 – roughly $8,000 in today’s dollars. But I had some wealthy friends, and when my boss asked if I had access to a Mac to beta test a new app he was building, I borrowed a friend’s machine and fired it up for the first time.

As I wrote in The Search, that moment changed my life. I instantly knew that this machine was the most important artifact ever created by humankind. I wanted to tell the story of its impact on the world. My first job out of college, at a startup magazine called MacWeek, was reporting on Apple and the ebullient industry surrounding it.

Apple in its early years was a pirate company, positioned as a counterweight to the hegemony of much larger companies that dominated the nascent computer industry. Microsoft and Intel were not just behemoths, they were evil, mendacious, and utterly corporate. Apple, on the other hand, represented creativity, human spirit, and independence. Those of us in the “Macintosh community” cast ourselves as morally superior underdogs – the heroes of an unimaginably exciting new story.

That’s not to say we didn’t view Apple as an adversary. My early career was driven by a reporter’s zeal to overcome the company’s famous secrecy. We reveled in scoops about the company’s new hardware, software, and business strategy. We scrutinized its every move and executive utterance. Our motivation was more more than reportorial glory – we felt it was our job to keep Apple on track, to ensure it would win in a world dominated by ugly companies with unsavory values. If, as we believed, all of society would someday be driven by this emerging digital industry, we wanted the good guys to win.

I map the rise of digital technology over the past half century in nine overlapping eras. Apple features prominently in most of them:

1970s–1984  — Early Personal Computing
1984–1990    — The Macintosh Era
1985–1993     — The First Online Services
1993–2001     — Early Web
1994–2002    — The Dot-Com Boom and Bust
2003–2012     — Search, Social and Web 2.0
2012–2018.    — Rise of the Oligarchy
2018–2022    — Consolidation and Political Power
2022–pres.    — The (Early) AI Era

I used Apple products in each of those eras, and I’m still using a Mac today. But I’ve avoided all of Apple’s products after it entered the smartphone market. I don’t use the iPhone, I’ve never relied on iCloud, and Apple’s app store remains a foreign destination. I switched to Google’s Pixel in 2012, and I’ve never looked back. The reason? I felt that Apple had taken its business strategy of vertical integration too far. With the iPhone, Apple began to act like all those companies it once railed against: A massive juggernaut bent on locking its customers into beautifully designed walled gardens.

The worst offender? The Apple App Store, where Apple dictated what software its customers could and could not use. Steve Jobs famously called mobile carriers “orifices” that locked their customers into paternalistic and deeply  misaligned relationships. With the App Store, Apple built the biggest orifice of them all.

Which leads me to why I decided to write about Apple today. As I laid out above, we’re now in the AI era of computing. Apple hasn’t exactly been a leading player in AI – but it’s certainly poised to be. The company wasn’t a player in search or social either, but thanks to its near death grip on distribution, it managed to profit handsomely from both those developments. The same strategy is playing out in AI. Those 1.6 billion active iPhones will all be running AI, AI that can only be accessed through Apple’s orifices. And Apple will happily make hundreds of billions in AI profit along the way.

Unless, of course, the AI ecosystem treats the app store as damage, and begins to route around it. That seems to be the case with AI coding apps, which allow end users to build, well, whatever the hell they want to build. That reads as dangerous to the Apple’s corporate interests, and yesterday, the company did exactly what one might expect a dinosaur to do when faced with mammals scurrying around its feet. It stomped. (It already stomps all over Mac-based AI coding, for more on that, see my last post).

50 years into the Apple revolution, the rebel has become the tyrant. There’s much, much more to say about how Apple is architecting control over the coming AI wave. But for today, on the cusp of the company’s 50th birthday, I’ll leave it at this: If Apple has its way, our industry, and our society, will be much the poorer for it.

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Last night I dreamt I was merging onto a rushing freeway. My on-ramp was far too short, a concrete embankment hemmed me in to the right. Faceless, speeding vehicles filled the lanes; integrating with them would require icy determination and perfectly executed timing. Missing the merge would bring certain death. The dream began after the point of no return – I was already accelerating into the flow, braking was not an option.

Do, or die.

My “writing brain” is often active during dreams, and as I sped toward that critical merge, a detached third-person narrator considered the meaning of my situation. This narrator simply knew that the speeding vehicles and the freeway itself represented the political economy of techno-capitalism – amoral, headlong, impersonal and ruthless. It also knew that my current reading of Sven Beckert’s Capitalism informed this perception – it’s a devastating history of the progress and the damage wrought by a revolution centuries in the making.

But what was I doing on this ramp, accelerating towards either certain death or exhilarating integration? My narrator had no theory on that question. I was simply acting. I looked over my left shoulder – an 18-wheeler barreled toward my path, I’d have to punch it and swerve in, hoping the truck would relent just enough to let me assimilate. I closed my eyes, floored it, and executed my play.

“We’ve seen this plot before, but not executing at this speed.“

That comment, left on my last post by a Professor of Entrepreneurship at Michigan, neatly sums up how I feel about the moment we’re in. He was responding to my observation that we’ve seen periods of extreme openness and experimentation in tech, but they’re always followed by consolidation and lock-in by corporations that leaves the ecosystem feeling poorer and less innovative. Today’s moment just feels much faster, and far more consequential.

Moving this fast is both exciting and troubling. It’s impossible to not break things at this speed. On the one hand, playing with AI feels exactly like the days of the early web – everybody’s tinkering, experimenting with new ideas and imagining new possibilities. But the pace is exhausting, as is the code-switching required to work with a strange new form of intelligence. We’re not taking the time to consider externalities or unintended consequences, and it often feels that we’re hurtling forward, slightly out of control, hoping it’ll all work out.

That dream is starting to make sense.

I started tinkering with Claude two years ago, but got serious about using it for projects just last year. One of my first ideas was to build a full-text database of everything I’ve ever written on this site. I’d then wrap that corpus with Claude’s chatbot interface. My goal was to use Claude to identify themes, arguments, and inconsistencies across the more than 1.5 million words and 5,800 posts I’ve written in the nearly 25 years since starting this site.

My initial attempts at building the “Searchblog Query Engine” ended in frustration and failure. Claude kept telling me it was possible, but I found it difficult to follow the steps it laid out – the technical chops required were beyond my skillset (and patience). I tried again this past January, after the release of Opus 4.5, and got much closer. At that point my issue was complexity – Claude wanted to do something called “vector embeddings” using OpenAI’s API. That made me nervous – I don’t like the idea of becoming dependent on anything from a company I don’t trust. After a few hours of noodling, I once again abandoned the project.

Earlier this week, I set out to try again. Instead of picking up from three months ago, I started fresh. This time Claude took a much more streamlined approach, walking me through the technical bits with patience and clear instructions. OpenAI’s API never came up  – I could use Claude’s instead. I knew just enough about API keys, the Mac’s Terminal application, and how scripts work to follow along.

45 minutes later, this was live:

I mean…holy shit. 

In less than an hour I built myself a tool I’d been dreaming about for years. What else might I build? What else might others build? Might this augur a world where tinkerers and dreamers once again lead us into an optimistic future, a future where platforms add more value than they extract? It’s hard to not hope for such an outcome, but harder still to reconcile such dreams with the present-day realities of platform policies, incentives, and power.

Toward the end of the Searchblog Query Engine project, Claude and I identified what felt like an interface bug. Each time I wanted to run the engine, I had to first open Terminal and execute a line of code. That felt non-intuitive, so I asked Claude for a workaround. “I can write you a small launcher,” Claude answered, “a double-clickable icon on your Mac desktop that starts the server automatically, so you don’t have to touch Terminal at all. Want that?”

“Hell yeah,” I responded. This would effectively turn my new creation into an app that lived on my computer. How cool is that?! Claude obliged, and a launcher called “Searchblog.command” just…appeared on my desktop. Huh, I noted, this is new. Claude now had root level access to my computer. That’s cool – I trust Claude and the work we’d done together.

But when I double-clicked on my new app, Apple begged to differ:

Apple gave me two choices: Move Claude’s offending piece of code to the trash, or hit “Done,” which ignored it altogether. Apple had taken control of my project, and left me unable to use it.

I have a fair amount of experience with how big tech platforms control access to their platforms, and while Apple’s warning was defensible, not offering me a way to bypass its prohibitions was inexcusable. Who’s in charge here – me, or Apple? I was on the final step of creating something I was genuinely excited about – a true bicycle for my mind, in fact – and Apple was treating me like an errant child.

I uploaded Apple’s admonition to Claude, which told me that Apple’s new MacOS, called Tahoe, “tightened Gatekeeper significantly.”

That got my attention. “What do you mean by “tightened Gatekeeper?” I asked.

“Gatekeeper is Apple’s system that checks apps and scripts before letting them run,” Claude responded. “It’s been around for years but each macOS version has made it stricter.”

Claude offered a workaround in Terminal, which I ran, and now the Launcher works flawlessly. But that experience left me a bit shaken. There’s an hidden world of code constraining what most of us can and cannot do with technology, and as we’ve seen again and again, those constraints almost always favor the business models of the tech platforms who enforce them.

I’ve researched and written about these constraints for years. But this one small example was the first time I’ve directly encountered their bite. As the world adapts and merges with the capabilities and complexities of AI, I’m certain it won’t be the last.

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It’s “phase two” of the AI boom, and the claws are out.

Back at the tail end of 2024, I wrote these words: “2025 will not be the year AI agents take off.  As the bloom came off the Generative AI rose in 2024, everyone started talking about AI agents as the Next Big Thing. Google, Apple, OpenAI, Microsoft, Meta, Amazon – all of them (and about a million startups) are trying to build user agents for both enterprise and consumer use cases. I’m a huge fan of the concept, but for now, it remains just that. Reasoning agents that book your travel, negotiate your insurance bills, or manage your calendar simply will not work if they are beholden to the same business models currently driving Big Tech.”

My prediction proved accurate – in 2025, anyway. But three months into 2026, it seems AI agents are not only everywhere, they’ve also got a mascot, and it’s a crustacean. Everyone in China, apparently, has gone all in on “raising lobsters” – using OpenClaw to automate nearly everything computer mediated. And as the Wall Street Journal reports this morning, lawyers, doctors, and entrepreneurs alike are racing to become power users of new agentic tools that lets them prompt their ideas into reality.

“Across the planet, everyone is tinkering,” notes Om Malik, usually one of tech’s most skeptical observers. Malik highlights what is perhaps the most important features of this year’s breakout trend: It’s not controlled by the tech oligarchy of Google, Meta, Apple, Microsoft, Amazon, and, more recently, OpenAI. OpenClaw, he writes, “represents a philosophy. The intelligence lives on your machine. You own it. You aim it. No subscription. No permission required.”

If this sounds familiar, you may have read my warning about the rise of generative AI three years ago: “We dream of genies, but who will they work for?” The piece lays out why I’m both excited and concerned about the potential of generative AI agents – they hold the promise of finally breaking us free of walled garden business models that have trapped all our data in profit-seeking amber. But if AI is driven by those same models, we may never have the chance to find out.

“Internet business models have been built to collect short term rent,” I wrote, then included a breakdown of OpenAI’s “terms of service” to prove my point. “Truly open systems rarely win over time,” I conclude, “regardless of whether the company uses the word “open” in its name.”

Add another one to the list: OpenClaw. Last month, aspiring tech oligarch OpenAI appeared to acquire OpenClaw — technically, its founder joined, but the narrative is clear. I understand why founder Peter Steinberger hitched his financial wagon to OpenAI’s rocket ship. He’s now a millionaire and his project will now have nearly unlimited resources. And OpenClaw has grown exponentially in the month since it became an “independent foundation” with OpenAI’s “support.” But let’s not forget — OpenAI itself was once an independent foundation “unconstrained by a need to generate financial return.”

That didn’t last.

For the moment, open, user-controlled systems like OpenClaw are dominating the tech conversation across society. It feels exactly like the early web – everybody tinkering, unconstrained by the dictates of corporations or governments. But we’ve seen this movie before, and it’s always ended the same way: Early enthusiasm for something new – be it home brew computers, web browsers, mobile phones, social networks, or app stores – always consolidates into the hands of ruthlessly capital-efficient corporations. It’s just never happened as quickly as it did with OpenClaw. I guess we have AI to thank for that.

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I was going to write a long piece on the implications of the ongoing cage match between Anthropic and the US government, but as I dug into the research, I realized that hot takes on subjects this complicated rarely add much value to the debate. I’m going to let things cool a bit and take another run at it down the road.

But something important kept tugging at me as I was reading up on what I believe is the most significant regulatory action ever taken in the tech industry (if you believe listing a major US company as a “supply chain risk” is NOT government regulation, you’re fooling yourself).

What kept coming up as I read all those hot takes was this: Anthropic finds itself at a unique and utterly novel moment in time, one that just might let it become a major consumer platform. To wit:

• Anthropic’s Claude is now the #1 downloaded consumer app for iOS, and recently broke into the top 5 on Google Play. Claude had languished below the top 100 at the start of 2025.
• Visits to the Claude website increased elevenfold in 2025 – from 16 million in January 2025 to 176.12 million in December 2025.
• While Claude’s increase in traffic in 2025 can largely be attributed to its original focus on B2B and enterprise usage, where it’s considered a leader, Anthropic’s management had the consumer in mind well before the current controversy. In January – before Anthropic’s cheeky Super Bowl ads, and well before the current Pentagon imbroglio, SimilarWeb estimated Claude.ai visits increased to 202.9 million, a 15 percent increase in one month. The February number, which will be published by mid-March, will likely show a much larger jump due both to the Super Bowl and the Pentagon news.
• The Super Bowl (on February 8) pushed Claude from #41 to #7 on the iOS App Store and drove a 32 percent US download spike and 15 percent global download spike. That momentum carried throughout February, with the app staying in the top 20 most of the month until the Pentagon conflict pushed it to #1 over the past few days.
• Yesterday, Anthropic announced two key upgrades to its Claude app, both of which are focused on the consumer: First, it added memory features to the free version of its app, matching OpenAI, and second, it added the ability to “absorb” the memory of competing apps, making switching from OpenAI or Gemini far less painful for consumers.

Anthropic has made its reputation – and its historic sprint to $14 billion in annualized revenue – on the back of a focused strategy that caters to enterprise clientele. It’s also built a brand based on being the more cautious and thoughtful of all the major model makers. It’s always had a good consumer app – I’ve been using it exclusively for more than a year – but until recently, the consumer market felt like an afterthought. In late 2025, OpenAI claimed 800+ million users, and Google’s Gemini had grown to 750 million. Claude’s users for the same period? A paltry 30 million.

But while Claude is tiny by comparison, it’s become a champion at converting free users to paid subscribers. Yes, most of those paid subscriptions were for business and enterprise use cases, but Anthropic is at a key inflection point: It’s got the world’s attention, it’s got a strong consumer value proposition – “we’re the good guys in tech, if you use us, your data won’t be used by the government” – and it’s already plowed the road to becoming a consumer brand with its Super Bowl ads and recently introduced competitive product features.  Kind of reminds me of another company in the early days of tech, one with tiny marketshare and a unique take on the world. (Yes, I mean Apple back in the 1980s).

Most observers of the AI industry estimate that Anthropic earns just 15 percent of its revenue from direct consumer subscribers. Given the past week’s news, I expect that number to change dramatically – if the Pentagon’s “supply chain risk” threat holds, enterprise revenue will slow dramatically, just as consumer revenue will inflect upwards.

What might it mean for Anthropic to become a consumer company at scale? For one thing, the company might have to reconsider its now-famous aversion to advertising. Time – and usage data – will tell. If Anthropic manages to retain the flood of new users checking out Claude, this fight with the US Government might prove to be the fulcrum to a major pivot in the company’s long term strategy.

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Earlier this week, as a major storm took aim at the little island where I live, I saw a story in which Sam Altman, CEO of OpenAI, defended his company’s energy use by comparing it to how much energy humans use to do similar tasks.

“…it also takes a lot of energy to train a human,” he argued, when asked about AI’s insatiable – and destructive – appetite for energy. “It takes like 20 years of life and all of the food you eat during that time before you get smart. And not only that, it took the very widespread evolution of the 100 billion people that have ever lived and learned not to get eaten by predators and learned how to figure out science and whatever, to produce you.”

It was late in the evening, and I was about to lose power, but the insanity of Altman’s comparison struck a nerve, and I wanted to call it out. I’ve mostly refrained from my old habits of dunking on idiotic shit through social media – back when I was on Twitter, I’d regularly engage in the practice. But I left Twitter when Altman’s fellow oligarch Elon Musk purchased (and ruined) the place, and in the past few years, I’ve started using LinkedIn as a home for various outbursts, most of them tame in comparison.

But Altman’s ridiculous statement got under my skin, and I reverted to my old Twitter ways. “What a total asshole,” I posted, along with a link to the TechCrunch piece.

The post began to gather steam, logging 15 comments and nearly 60 likes in its first half hour.  Most folks agreed with my sentiment, but a few pointed out that my comment was not entirely in character. “Not on Sunday please ” pled one commentator. Another responded, quite reasonably, with this: “Curious, given your background in writing and thinking through complex problems, there are some clear logical fallacies in Altman’s argument worth dissecting. What drew you to name-calling over that analysis?”

He was right. I didn’t have the time to write a proper post about the topic, as the storm had already taken down several trees nearby and we were busy laying in firewood for what turned out to be a four-day power outage. So I dashed off an apology of sorts: “You make a completely fair point. I’ll try to do better.”

I left it at that, the power went out, and for the next few days I forgot about the incident.

But checking my mail yesterday, I got my second-ever takedown notice from a social media site (we’ll get into the first in a minute). “Your post doesn’t comply with our Professional Community Policies on bullying and harassment. It’s been removed from LinkedIn and only you can access it.”

A social media site that removes posts for name calling?! I suppose it does make sense. LinkedIn is a professional network, and calling someone an asshole is certainly not professional. It’s not easy to impose “community standards” on a platform of 1.2 billion people, and I’ve got no issues with this particular slap on my wrist.

As for Twitter (nee X), well, that’s another story. My first ever violation of a social media site’s community standards came in late 2022, as I was both leaving Twitter and setting up an account at Mastodon, an open source, federated version of Twitter. Here’s the offending post, which was cross posted to Twitter from my Mastodon handle:

For those of you with a sense of history, I was poking a bit of fun at Elon with that post – the very first post on Twitter was from co-founder Jack Dorsey:

Well, Elon’s new team did not like my sense of humor, apparently, nor did they appreciate my linking to a direct competitor. My post was flagged for violating community standards, and degraded in search and X’s algorithmic feed.

What a bunch of assholes!

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Well, they’re here. Just a quick note for now (lots more to say later, but a board meeting in SF means that’ll be later) – OpenAI is rolling out ads to its free and “Go” paid tier. The ads look…harmless enough, just a sponsored link unit with small graphics at the bottom of the chat. Pretty much the exact launch playbook we saw from Google 25 years ago, and Facebook in 2012. A rudimentary prototype of what will become, over the next few years, an increasingly sophisticated monetization platform that, let’s face it, will probably make Instagram look tame.

OpenAI also rolled out some pledges: “We decide which ad to show by matching ads submitted by advertisers with the topic of your conversation, your past chats, and past interactions with ads. For example, if you’re researching recipes, you may see ads for meal kits or grocery delivery. If there are multiple advertisers, we’ll select the one that is most relevant to your chat to show you first….Advertisers do not have access to your chats, chat history, memories, or personal details. Advertisers only receive aggregate information about how their ads perform such as number of views or clicks.”

If you line up OpenAI’s pledges, it sounds awfully familiar: We won’t sell your data … but we will lease it in aggregate and target you personally. Ads won’t effect chat results … but we reserve the right to “evolve our advertising program to support additional formats, objectives and buying models and build new ways for businesses to interact with consumers in ChatGPT.” Truck, meet wide open hole.

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“Claude is a space to think,” the company announced in a blog post that promised to never let advertising creep into its core consumer product. “The history of ad-supported products suggests that advertising incentives, once introduced, tend to expand over time as they become integrated into revenue targets and product development, blurring boundaries that were once more clear-cut. We’ve chosen not to introduce these dynamics into Claude.”

This is exactly the point I was making in yesterday’s post – “Advertising Built Generative AI. Now Comes the Remodel.” And while Anthropic’s written post is both thoughtful and measured, the company also launched a four-pack of ads illustrating its point – ads that they will be running during the SuperBowl this weekend. Yep, the SuperBowl.

In the videos, Anthropic’s messaging is anything but subtle. Here’s one of them, “Betrayal,” where a pitch-perfect, dead-eyed AI therapist pivots from a question about a patient’s mother to a hard sell for a MILF dating site:

And here’s “Violation,” in which an eerily ripped AI assistant tries to sell shoe lifts to a young man looking to build muscle:

“Deception” plays on the same theme – a gratuitous AI chatbot tries to sell an entrepreneur on a payday loan scheme:

And in “Treachery” an AI professor counsels a student to celebrate turning in a good essay by treating herself to jewelry.

Anthropic knows exactly what it’s doing by tacking into the AI ads debate, and I can only imagine the fits these ads are giving its main competitor OpenAI. Actually, thanks to social media, we don’t have to wonder – OpenAI CEO Sam Altman couldn’t help but respond, and clearly, a nerve has been struck.

Well played, Anthropic. Now let’s see if that SuperBowl spend delivers a positive ROAS (that’s Return on Ad Spend, for those of you taking notes…).

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