Wait, Chat Is Dead? Does That Mean OpenAI Is Abandoning Ads?

Over the weekend the Financial Times came out with a report on OpenAI’s latest pivot.  According to a senior OpenAI executive quoted in the piece, the company has decided that “chat is dead.”

Instead, company executives insist, the future lies in a “super app,” an agent (from OpenAI, naturally) that will do everything for us. The “surface” – the interface between a user and OpenAI’s service – will no longer be a fixed chat box. Instead, according to Thibault Sottiaux, who now heads the OpenAI super app project, “what we’re building towards is where you have your own personal agent that is capable of helping you . . . across everything in your life, be it personally or at work.”

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Where’s All the AI Magic?

“Hey Google, how are the Giants doing this year?” 

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.

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Google Encloses The Web

Last month Google announced the most significant change to its search product since its launch in 1998. Its iconic search box, which I’ve long compared to a command line for the Internet, has been redesigned to incorporate multi-modal chatbot capabilities. In essence, Google is no longer going to send you off to the best possible destination for your query. Now it’s built to capture your input and convert it into answers (and actions) all in one place – on Google.com.

Google’s announcement was expected – the company had to compete with the new paradigm of “answer engines” from OpenAI and Anthropic. But when the shoe did drop, my inbox filled with trepidation. Google has been the beating heart of the Internet’s circulatory system. Now that it’s evolved into a self-contained walled garden, how will the open web survive?

I’ve read every hot take and every second-day analysis of Google’s move. They all point to the same conclusion: As Casey Newton put it, the web is being “summarized to death.” Sure, there’ll still be websites, but the grand bargain with Google – free content for free traffic – is over. Google has  enclosed the entire world wide web and turned it into a walled garden which it alone can monetize.  As Enrique Dans put it over on Medium, the web is no longer a destination, it is merely “raw material.”

But there’s a nagging question lurking in all this hand wringing. Google’s search service, as well as LLMs like Gemini, ChatGPT and Claude, are all built on the back of the web’s open architecture. For two decades, the grand bargain insured that at least some of the economics flowed back to the people who created those sites in the form of traffic, which could be converted into advertising, subscription, and other forms of remuneration.

If Google encloses the web and starves it of oxygen, won’t that ultimately prove bad for Google itself?

I posed just that question (see screenshot, above) to Google’s new AI search feature. It dutifully came back with four categories of answers:

1. Embedded AI Advertising – “As users rely on AI for direct answers rather than clicking links, Google integrates advertising into the AI generation process.”

This is already well underway. Put another way, Google will create ads on the fly on behalf of its customers (the advertisers), and surface them directly inside the AI search experience. Think Instagram, but in search. Yay!

2. The “Walled Garden” Ecosystem. “Google doesn’t need an open web if it owns the environments where users spend their time.”

Yep. Pretty much the flip side of #1.

3. Enterprise Infrastructure & Licensing. “Google’s monetization is diversifying beyond advertising, transitioning into a massive tech-infrastructure and subscription company.”

True, but advertising is still king.

4. Proprietary Data Monopolies. “Rather than crawling independent websites, future web experiences will increasingly rely on proprietary licensing deals and AI agent-to-agent interactions.”

Now this is where it gets interesting. To feed their increasingly ravenous AI maws, Google (and other contenders like Anthropic and OpenAI) are paying up for “raw materials” to ensure their products have fresh and accurate information. This is a “business development first” approach to information: aggregators who have captured our attention will decide which information suppliers are worthy of ingestion. Those suppliers are then relegated to a fixed-margin business at the mercy of their upstream overlords.

Is this sustainable? Is it good for a free and open society that demands quality information to thrive?  It certainly doesn’t feel that way to me, or to nearly anyone who’s thinking deeply about an information ecosystem absent the level playing field that Google search used to provide.

“I don’t think anyone really knows what this means,” wrote Benedict Evans in his May 26th newsletter. Sadly, I concur.

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Data Is Non Rivalrous. Why Have We Enclosed It?

One of the many reasons I’m a fan of reading history is its ability to offer frameworks for understanding the present. I recently finished Sven Beckert’s Capitalism: A Global History, a 1,300-page monument to scholarship that The New York Times praised as “generational” in its importance. I tend to agree. Its pages contain foundational truths which enliven today’s debate around the role of technology in society.

Beckert argues that over the past millennium, capitalism’s amoral ideology of “accumulation above all else” has become so deeply embedded in the global political economy that we no longer question its core assumptions.

We are the fish, capitalism is the water.

But as Beckert demonstrates, capitalism’s march to omnipresence was a jagged one, filled with reprehensible and often horrifying demonstrations of state, corporate, and personal opportunism at a global scale. If, for example, you had any doubts about the central role slavery played in the creation of the modern industrial economy, Capitalism should dispel them*.

But this post is not a review of the book – I highly recommend it, should you be so inclined. Instead, I want to think out loud about a concept central to its argument: enclosure.

The formal definition of “enclosure” is “the removal of common rights that people held over farm lands and parish commons.” The term is usually associated with the evolution of English society from the late 1500s through the early 1800s, a time when the country transitioned from subsistence-based farming to a market- and export-driven economy. In the name of productivity and profit, and with the enthusiastic support of the monarchy, capitalists enclosed lands formerly held as public commons, forcing a new class of tenant farmers and wage laborers to produce agricultural products for markets opened by the rise of global trade.

While the English may have invented enclosure, they certainly did not have a monopoly on the practice. If we redefine enclosure as leveraging law, violence, or economic pressure to acquire commodity and/or free inputs to drive capitalist outcomes, the list grows well beyond agriculture. Throughout his work, Beckert delivers example after example of capital enclosing nearly all natural resources, including minerals, water, timber and fossil fuels.

Crucially, the practice of enclosure was not limited to commodities. By the mid 1800s, human labor had also been violently enclosed, either through slavery, indenture, indebtedness, or the relatively new practice of wage labor. Beckert demonstrates that the industrial revolution – and our heritage as a capitalist economy – is a byproduct of this enclosure. The modern state, with its ability to wage war and coerce compliance through lawfare, was central to enclosure’s success.

Reading Beckert helps us understand powerful and largely invisible forces driving assumptions behind today’s technology- and information-driven political economy. We learn that capitalism loves nothing more than inexpensive (and if possible, free) inputs which it can turn into profitable market goods. For centuries capitalism built a global economy based on these inputs: labor, cotton, saltpeter, indigo, coal, iron, and oil, among countless others.

These resources all share one critical characteristic: they are rivalrous. A ton of coal or the labor of a worker may power my factory or it may power yours, but it cannot power both. Once it’s used, it’s gone. The same can be said for an acre of land, a bushel of corn, or a roll of steel. Capitalism was built on the concept of rivalry – an endless competition for the non-renewable resources upon which wealth is built.

Beckert’s examination of capitalism necessarily ends just as the information age is gathering strength. But his work leaves me certain that regardless of the changes that digital technology has wrought, one thing remains constant: Capitalism covets and encloses valuable inputs – and once enclosed, capitalists fights like hell to maintain that enclosure.

WHAT ABOUT DATA?

In data, capitalism has found a novel, elastic, and invaluable new input. In an astonishingly short amount of time and just as it did with physical commodities, capitalism has enclosed this new asset and claimed it as its own**.

Whether you are nodding your head or rolling your eyes at that sentiment, it’s hard to argue that the aggregate value of the world’s data is anything but central to our information economy. That we’ve ceded this power to corporations without fully investigating alternative architectures of control will be seen as one of the greatest mistakes of the post-digital era, and the apotheosis of regulatory capture via mechanisms that capital has long used to dominate the state.

Why label our current approach to managing data as a societal asset a historic mistake? It’d likely take at least 1,300 pages to definitively argue that point, but in this post I’ll focus on this one fact:

Data is non-rivalrous.

The Corporate Finance Institute defines non-rivalrous goods as “public goods that are consumed by people but whose supply is not affected by people’s consumption. In other words, when an individual or a group of individuals use a particular good, the supply left for other people to use remains unchanged. Therefore, non-rivalrous goods can be consumed over and over again without the fear of depletion of supply.”

Data are like ideas – if I give you a copy of mine, you gain, but I do not necessarily lose. Centuries before the concept of “data” took root, Thomas Jefferson wrote of ideas:

“That ideas should freely spread from one to another over the globe, for the moral and mutual instruction of man, and improvement of his condition, seems to have been peculiarly and benevolently designed by nature, when she made them, like fire, expansible over all space, without lessening their density in any point, and like the air in which we breathe, move, and have our physical being, incapable of confinement or exclusive appropriation.”

Only 20 years after the British passed the Inclosure Act of 1773, which enabled enclosure of land and the removal of the right of commoners’ access to that land, Jefferson laid the groundwork for an enlightened approach to data.

Shame on us if we decide to ignore him.

*And if you want to go deeper, read Beckert’s widely praised history of the cotton trade, Empire of Cotton. 

**I’ve written about this practice continuously over the past 20 years, but I’ve not definitively linked it to the concept of enclosure. In future writings, I’ll detail how the technology industry, with the full throated support of most western governments, has used Terms of Service and Privacy Policies to enclose data for its own enrichment, and to the detriment of a more flourishing society. 

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OpenAI Plans on Marketing Its Way To Glory. Bonne Chance!

The cookies have it.

Early this past Saturday morning I got an email from OpenAI titled “Update to our privacy policy and more controls.” I don’t recall ever getting email from the company – I signed up for ChatGPT when it launched, but haven’t used the service much since switching to Claude several years ago. But the email reminded me of a story I read from The Information last week, and I think it’s fair to say the two are related: OpenAI Sees $8 ChatGPT Driving Consumer Subscribers to 122 Million This Year.

I’ve written several posts about OpenAI’s jaw-dropping advertising ambitions, which I believe history will judge as the most audacious and potentially damaging expansion of the Internet’s data-driven advertising model since the invention of AdWords, Google’s original cash cow. OpenAI plans on scaling its advertising revenue from zero in 2025 to more than $100 billion by 2030. As I pointed out earlier, it took Google nearly two decades to reach that milestone.

The Information’s reporting gives us some insights on how OpenAI is planning to hit those lofty goals. Step one is to build out as much advertising inventory as possible by leaning on its free and low-cost subscription models. According to The Information, OpenAI is forecasting that “consumer subscribers to ChatGPT Go, which costs $8 a month in the U.S. and around $5 monthly in other countries such as India, would surge about 36 times to 112 million this year.”

If those numbers make you shrug, pull those shoulders down, and let’s do a bit of math. Two months ago, OpenAI announced it has around 50 million paying subscribers – 62 million fewer than its goal of 112 million for this year. That’s quite a mountain to scale.

But OpenAI is the fastest growing consumer application in the history of the Internet, no? Well, yes, it was. Now? Not so much. Over the past few quarters, OpenAI’s subscriber growth has hit a wall. A report in the Wall Street Journal last week put it starkly: “OpenAI missed an internal goal of reaching one billion weekly active users for ChatGPT by the end of last year, according to people familiar with the goals. The company still hasn’t announced that milestone, unnerving some investors. It also missed its yearly revenue target for ChatGPT as well after Google’s Gemini saw massive growth late last year and ate into OpenAI’s market share.”

So how does OpenAI plan on adding 62 million new paying subscribers this year? That’s where that Saturday morning email comes into play. The email main point is to inform us that OpenAI is now using cookies, those much-maligned pieces of code that drive programmatic advertising across the Internet. “We wanted to let you know that we’ve updated our Privacy Policy to include how we use cookies and other similar technologies,” read the first line of the email.

At first, I figured OpenAI was adding cookies to ensure its still-nascent advertising platform would meet with the expectations of nearly all of its potential marketing customers. Regardless of repeated efforts to kill them, cookies still form the backbone of how marketers measure ad performance. Why is OpenAI making such a big deal about using them?

That’s when it hit me: OpenAI plans on marketing itself out of its subscriber growth problem. Here’s another line from that aforementioned email: “We’ll now use cookies to promote OpenAI products and services on other websites.”

Will they ever! Dig into the company’s updated privacy and cookie policies, and you’ll learn even more: OpenAI employs hundreds of cookies, including more than 40 “marketing measurement” cookies placed on six distinct third-party websites. Those sites – LinkedIn, Reddit, Meta, Google, TikTok, and Bing/Microsoft* – form the foundation of today’s Internet advertising infrastructure.

Put another way, OpenAI is going to use aggressive, performance- and data-driven Internet advertising tactics in an attempt to build the world’s fastest growing … performance- and data-driven Internet advertising business. It reminds me of how TikTok built its US business by flooding Facebook and Instagram with ads to drive TikTok downloads. But unlike TikTok, which has a free service, OpenAI has to convince 62 million more folks to pay a subscription fee. Bonne chance!

We all thought OpenAI was using all that recently acquired capital to build more data centers, but at least a few billion of those dollars will certainly be aimed directly at the company’s much more pressing problem: Acquiring customers. Grab some popcorn and get ready for a blitzkrieg of OpenAI marketing, folks. This will be a show worth binging.

*I can only imagine the folks at Apple and Amazon are pissed they’re not included on OpenAI’s initial media plan. Not to worry, I’m sure they’ll get optimized in!

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Where’s My AI Shareware?!

Back in the day.

Have you noticed all the folks bragging about the cool new tools they’ve hacked up using AI? In the last month or so, I’ve read newsletters from half a dozen or so people detailing vibe-coded apps that help them do research, organize their life, or even build entire websites. And it’s not just media types who are building things. As I wrote about earlier, my son built a custom CRM system for his company over one weekend. That kind of capability is impressive. It feels like something new and big is underway.

Which got me thinking. If we’re all hacking up these cool tools, how come we can’t share them with each other? Why are we all consigned to re-invent the wheel each time we want to build, say, a “Searchblog Query Engine“?

Back at the dawn of digital technologies, when the PC was young and the Web a dream in the distant future, a vibrant sharing culture emerged around the Macintosh. I was part of the early Mac scene in the mid to late 1980s, and I vividly recall the excitement of getting new software utilities from friends and colleagues in the industry. These were neat hacks built by passionate tinkerers and validated by peer review and word of mouth.

Most utilities came via “sneaker net” – IE, they arrived via a beat up 3.5-inch floppy disk carried from one user to another. They often came with a business model known as “shareware” or “freeware” – you could use the utility for free, and you’d pay the developer only if you felt you were getting value from the product.

Shareware filled in the many holes left by commercial software providers at the time – and often pointed the way to entirely new markets that would later become billion dollar opportunities. We all installed “Disinfectant,” an early anti-virus utility (its maker refused to accept payment.) And given how precious disk and RAM space was back then (the average Mac had about 128K of RAM and a 20-MB hard drive), compression utilities were one of the first to break out big – we had our choice of Stuffit, DiskDoubler, and dozens of others. SuperClock! put a clock in your menu bar – at the time, that was a very big deal. After Dark gave us customizable screen savers. And Quick Keys let you automate various tasks via your keyboard – a precursor to the era of agentic AI we are now entering. We could download custom sounds, fonts, and games – and we took pride in souping up our machines with these novel hacks.

So now that we’re in the era of vibe coding, I gotta ask: Where are my AI utilities?! When Troy makes a media distillation utility, or Mario makes a “knowledge dashboard,” or for that matter, when my son Ian makes a CRM, why can’t they easily share their work for fun or profit? Something feels broken around the time-honored practice of tinkering in our current AI culture. And I think I know why.

Back when the Mac was new, no one controlled distribution. Anyone could make a copy of software on a disk, and everyone did it, all the time. The ensuing software industry quickly invented copy protection, but that was fine – none of the utility makers employed it. User groups like BMUG became arbiters of taste when it came to utilities, building sharing libraries and user reviews as part of their services*. Sharing was built into the culture of early computing.

Today’s computer culture, if we can claim to have one at all, is built on the ethos of profit and extraction. If you build an awesome agent using Claude, sharing it is structurally difficult. Anyone using your agent has to have a Claude subscription. Plus, they’ll need to recreate your agent’s core integrations from scratch, because their computer and web usage will be different from yours. If you make an app, well, now you have to play Apple or Google’s app store games – good luck getting found without paying a marketing tax, not to mention the 15-30 percent cut the platforms will take. Oh, and they can deny your app entirely, or pull it once they realize it might threaten their profits.

I could write thousands more words on this, but I think the point is made: Our current technology landscape is hostile to the freewheeling sharing culture which gave birth to the digital revolution. Perhaps that’s just what progress looks like, but to me, it’s a little bit sad.

* I even edited the BMUG newsletter for a couple years during this time.

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Your Conversations With AI Are Now On Sale

OpenAI’s early Ads Manager interface, as posted on Search Engine Roundtable.

Data-driven performance advertising built the modern internet, warts and all. Data has become the most valuable resource in our economy, and the world’s most profitable companies have all organized around enclosing, extracting, processing, refining, and exploiting this new asset class.

Yesterday, OpenAI released its first performance advertising product. Marketers can now purchase “cost per click” advertising on ChatGPT, which means they can compare how money spent on OpenAI measures up to similar platforms like Google, Meta/Instagram, Apple, and Amazon, among many, many others. And if OpenAI’s offerings fail to compete, the company will have no choice but to modify its products to drive better performance.

Put simply, the race is on, and it’s one OpenAI can’t afford to lose. The data we create as we pour our hopes, fears, intimacies, questions, and personal narratives into the insatiable maw of an AI chatbot is being enclosed and exploited by the very same business model that bequeathed us Facebook.

It was inevitable that OpenAI would meet the Internet at its most profitable nexus. Now that it has, the incentive structures of performance advertising will forever imprint the fabric of our interactions with AI, and by extension, our understanding of the world.

Google’s introduction of cost-per-click a quarter century ago sparked a revolution in marketing that has shaped every corner of the digital world.  Not only have the search, social, and mobile industries been built on the back of performance-based advertising units, so have the consumer products that shape our culture: Instagram, Amazon, YouTube, Reddit, Twitter, and of course Google search. An obsession with performance birthed the data-driven “surveillance capitalism” now ubiquitous to nearly every business model on the Internet, from Uber to Apple (and yes, Apple collects and leverages truckloads of data to deliver both advertising and other services).

Given this, the question now becomes: How will the incentives inherent in data-driven advertising impact our experiences with AI? To presume nothing will change is to ignore history and the basic tenets of capitalism. OpenAI has declared ambitions to become a $100 billion performance advertising business in less than four years (it took Google almost two decades to reach that milestone). OpenAI also plans on becoming a trillion-dollar public company by the end of this year. Those kinds of expectations will inevitably force Sam Altman and his team to tailor their consumer products toward the collection and exploitation of their company’s most precious resource: The information we all disgorge into billions of chatbot windows each day.

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Rebel, King, and Tyrant: Apple at 50

Wired, 1997: We were genuinely worried the company would go out of business.

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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We Dream of Genies, But Will Big Tech Let Us Use Them?

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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How Long Will Your Claw Be Open?

Try me, then you’ll buy me.

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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2 Comments on How Long Will Your Claw Be Open?