That quiet part? In an internal memo leaked to 404 Media, a Microsoft VP said his team’s goal was to “make people addicted” to Microsoft’s new Scout tool, which is fashioned on Open Claw, the AI agent project that went viral early this year. Nadella quickly quashed such sentiments, releasing a memo stating “this is absolutely a non goal! If anything we are doing the exact opposite. We want to make sure AI empowers and adds real value to human endeavor and broad economic growth! We should make our teams clear about this.”

Microsoft then went into damage control mode, with corporate comms chief Frank Shaw piling on that Scout is for “helping people accomplish tasks more effectively—not encouraging dependency. Our goal isn’t more screen time. It’s more time back.”

The language of addiction has come to dominate criticism of big tech’s approach to product design, particularly for algorithmically driven apps like Instagram, YouTube, and TikTok. It’s a proxy for tech’s advertising business model, which has fueled the growth of the industry for decades.

That the dustup centers on Microsoft is what makes the story stand out – of all the big technology companies, Microsoft has the smallest advertising business and the worst track record of creating addictive consumer applications (if you’re addicted to LinkedIn, which Microsoft acquired a decade ago, I recommend you put down the phone and seek help).

“Luckily for us, Microsoft is pretty bad at making addicting products compared to some of the other big companies” quipped an anonymous Microsoft employee in the 404 Media report.

The addiction narrative is picking up speed across the AI industry. Kids addicted to their chatbots, adults addicted to AI companions, coders addicted to Claude code. With OpenAI building an advertising platform largely modeled after Meta’s Instagram, the power of that narrative will only increase. Nadella is wise to distance himself early, loudly, and often. We don’t need the models of the social media era dictating the products we build in the AI era. AI should work for us, not on us.

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“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.

Nope.  

“The San Francisco Giants are in fourth place in the NL West.” 

In a tech world driven by generative AI, that’s a pretty lame answer. So I headed into my study and asked Google on my computer. Better: A search one box showing the Giants’ place in the NL West (it’s not pretty). Sure, there were the traditional blue links below, but that one box gives me a better sense of the answer. 

Still no AI magic, however. I pulled out my phone and asked Google in “AI Mode” – a habit I’m not yet in, but one Google is pushing through incorporating AI into its core search offerings. Now I get something quite different. A chatty AI agent summarizes any number of relevant angles on my team’s dismal performance:  

Why am I on about this? Well, I’ve been mulling the role that basic economics plays in the unfolding story of AI in our daily lives, and I’ve come to realize that the magic of AI isn’t coming to save us any time soon, at least not when it comes to what pundits like to call “ambient intelligence.” 

This all relates to what feels like a significant vibe shift in the AI landscape. Three-plus years into what was sold to us as a revolution, AI remains mostly tethered to its original form factor: a helpful if somewhat frustrating chatbot that lives on your phone or web browser. Yes, we’re finding more and more things to do with this new pal of ours, but for many of us, AI is losing its magic. 

Why? Blame economics. Serving up AI answers remains an extremely expensive endeavor. Rationally, firms with the wherewithal to deliver AI are productizing their services in places they believe will provide the maximum opportunity to deliver a return. For Google, that’s the search box and their Google Workspace surfaces. For Meta, it’s engagement bait to keep people on Instagram and expansion into business markets. Over at Microsoft, it’s all about enterprise applications. Amazon? Commerce, of course. And Apple? Still waiting it out, though it seems they’re on the brink of a better Siri.  

In other words, so far it’s been more of the same. And more of the same isn’t exactly magical. In fact, it’s starting to feel like we’re being steered, and we kind of don’t like that. 

Claude is my AI chatbot of choice, so I asked it the same question. Its answer:

Not good, John — they’re 23-38, dead last in the NL West and essentially tied for the worst record in the National League (only the Angels are clearly below them; the Rockies are a hair ahead at 24-38). They’re sitting roughly 16 games back of the Dodgers in the division, which at this point in June is less a deficit than a verdict.

The recent stretch tells the story in miniature. They hung 19 runs on Colorado on May 31 — which sounds great until you remember Colorado is a 24-38 team and Coors Field flatters everyone — and then ran straight into Milwaukee, the best team in the NL at 37-21, and got outscored 24-5 across the first two games of the series. That’s the pattern of a bad team: they can beat the other bad teams and get steamrolled by anyone good. A 19-run outburst against the Rockies doesn’t tell you much; getting held to 2 and 3 runs by a real pitching staff does.

On prospects for the next couple of months, here’s the honest read, and I’ll flag where I’m reasoning versus where I actually know something. The schedule ahead is survivable but not soft: they finish at Milwaukee, then a four-game set at the Cubs (32-29, a legit team), then home against Washington (a sub-.500 club they should split or win). So there’s a chance to pad the record against Washington, but the Brewers and Cubs portions are likely to hurt. None of that changes the structural problem, which is that a team 15-plus games out in early June has, historically, essentially no realistic path back into contention — the math of needing to play .650 ball for four months when you’ve played .377 for two just doesn’t bend.

Now that’s a damn fine answer, and I’m paying $100 a month to get it (I’m on Claude’s “Max” plan). But wouldn’t it be cool if I got that kind of a response on my Google Home? After all, that’s how this whole thing started, back when I was brushing my teeth at my bathroom sink. I have a notion, I speak it into the air, and the room fills with an intelligent and engaging answer. I’ve essentially summoned a seasoned baseball color commentator into my bedroom, and I can speak with him with all the fluency of Captain Kirk on the bridge of the Enterprise.

Computer! What’s wrong with the Giants?!

Well John, how much time do you have?!

Magic!

Well, that ain’t gonna happen. First, Google is never going to export its Gemini service to its Home business, because the economics simply don’t work. There’s no ad surface to speak of, and no one is going to pay a subscription fee for a home speaker, especially if they’re already paying for Google Workspace, OpenAI, or Anthropic’s AI offerings. 

And while it’d be cool, I can’t imagine Anthropic or OpenAI getting into the home speaker business. Anthropic makes most of its money helping enterprises code, and OpenAI is on a path to becoming an advertising business. Again, it’s hard to build a viable ads business on top of a home audio device. 

I’m belaboring this example to make a simple point: The idea of “ambient intelligence” is super cool in theory, but dead on arrival in practice. And that fact won’t change until the economics of delivering AI change by several orders of magnitude. I’m still six months from grading my 2026 Predictions, but I’m pretty sure I got this one wrong. 

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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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All aboard ha ha ha ha ha ha ha
AI, AI, AI, AI, AI, AI, AI!

– Crazy Train, Ozzy Osborne (with slight modifications)

***

If you can remember the early web, when the tech was novel and browsers a revelation, you probably remember a feeling distinctly absent from today’s AI fever dream:

Connection.

The web was a distinctly “we” medium. We downloaded the first MOSAIC browser, and we chortled together as we found new sites to visit. Each link was a connection not only to knowledge, but to others – there were human beings behind those rudimentary web pages, their voices strong through the choices they made in layout, text, and tone.

When we made web pages – and we made a lot of them – we made them for others. We imagined who might visit, how they might react, the impact of our work. We hoped to spark dialog. Our sites were invitations, and when people came over, we treated them like new friends.

For the early web pioneers, the Web was a Cambrian explosion of social connection. It’s one of the reasons publications like Wired and The Industry Standard flourished – they became touchstones for the Web community.

Now think about how you use AI.

Woof. 

Engaging with AI is a profoundly isolating experience. Its initial product offerings felt human – we were conversing with what seemed to be a sentient being, after all. But by now we’re all (well, mostly all) in on the joke: We’re whispering to machines all by ourselves.

I’m no AI detractor, I employ it regularly. But it’s a solo pursuit – research, work tasks, thinking out loud. By this point I’ve made up my mind about the technology: It’s a fabulous tool, but it’s not a person, nor is it a replacement for one.

Om Malik  wrote a piece today criticizing on the Valley’s obsession with digital “twins.” He notes that Reid Hoffman, who I have known and admired for decades – has created an AI twin that he claims scales his personage. “I would much prefer two minutes with the actual Reid Hoffman,” Om writes, “than hours of engagement with Reid AI. In two minutes, we could end up in a conversation that goes somewhere neither of us expected.”

Humanity is messy, non-linear, and surprising. When those surprises hit us, we must react – there are consequences to human engagement. If the real Reid Hoffman tells me he believes AI is fundamentally misunderstood in society (as he did recently), I can challenge him, and perhaps change his mind. Reid AI? Not so much.

Which leads me to wonder: Are there fundamentally social use cases for AI? I don’t mean the spectacularly ill-advised Sora, or the underlying AI driving our serotonin addictions on Instagram or YouTube. I suppose the formation of yet another cultish group house featured in The New York Times kind of qualifies, but I want examples of actual AI applications that truly connect us in ways that add value to all involved.

If you’ve seen this in the wild, please let us know about it. But at this point,  AI feels like a deeply anti-social technology.

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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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Today my partners and I are launching the program driving our third annual DOC summit, to be held in Sonoma, CA later this fall. When my close friend Dr. Jordan Shlain brought me his idea for a new kind of gathering back in 2023, I had no clue how much I would learn. It wasn’t just about medicine and healthcare science, but also about creating a community. It’s been a great journey so far, and in year three, it will really take off. 

One reason is that while I’ve been the acting CEO for DOC’s early years, we now have a full-time, seasoned pro taking over: Dr. Neil Parikh, who joined us last month. Not only is Neil an entrepreneur, a physician, and an MBA, he’s also got the even temperament and quiet intelligence that commands a room through earned respect. With Neil at the helm, Jordan and I can focus on what we most love to do: bring a community together through the expression of a fantastic program. 

The magic of DOC isn’t in its headliners or its location, though both are fabulous. It’s in the care we take in assembling the people who gather each year. Among the 300 members of our community are scores of physicians, scientists, investors, and business leaders who all share a passion for identifying, scaling, and supporting the highest-quality science behind the medical breakthroughs that have the potential to effect meaningful change, right now. They all are accustomed to keynoting conferences and events around the world. That’s why we consider each DOC program a conversation, not a dictation – and why every participant may interrogate the proceedings at any time with questions or insights that add to that conversation. 

We have something of a “dinner party rule” at DOC – we only invite people we’d want to sit next to at a table filled with fascinating folks. We find these unique individuals via word-of-mouth – through our advisors, colleagues, past participants, and friends. It may not be the most efficient way to build an “events business,” but it’s certainly the best way to build a lasting community. 

This year we’ll be focusing on a host of topics and issues at the forefront of our community’s minds: The impact of AI on medicine. The state of our healthcare system. Breakthroughs in neuroscience that push the boundaries between our minds and the emerging intelligence of artificial intelligence. Advances in women’s health. Joining us to push our thinking will be leaders from government, science, business, and finance, including:

• • Alicia Jackson, Ph.D., Director of ARPA-H. Dr. Jackson is responsible for determining where her agency will place billions of dollars in long term bets on the future state of medical science. 
  • Eddie Chang M.D., Chair of Neurological Surgery at the University of California, San Francisco, whose pioneering work on brain-computer interfaces is redefining the field. 
  • Laura Esserman M.D., MBA, Principal Investigator of the WISDOM Study and Director of the UCSF Carol Franc Buck Breast Care Center, who will report on the initial findings from her ground breaking research on breast cancer detection and risk reduction. 
  • Travis Zack M.D., Ph.D, Chief Medical Officer, OpenEvidence. Zack is the medical mind responsible for the astonishing rise of OpenEvidence, one of the fastest growing and most respected AI companies in the world.  
  • Helen Messier M.D. Ph.D., Chief Medical Officer of Bioscope.AI. Dr. Messier is a physician-scientist and leader in precision and longevity medicine, working at the intersection of molecular biology, clinical care, and new models of care delivery.
  • Larry Brilliant, M.D., a renown epidemiologist, technologist, philanthropist, and author, now CEO of Evity.AI, a startup integrating artificial intelligence with epidemiology and precision medicine.
  • Nicole Gaudelli Ph.D., a life science entrepreneur in residence at Google Venture best known for her work in creating the Adenine Base Editor, a significant advance on the CRISPR gene-editing framework. 
  • George Yancopoulos, M.D., Ph.D., Co-founder, Board co-Chair, President and Chief Scientific Officer, Regeneron. Dr. Yancopoulos is widely recognized as one of the most successful entrepreneurs, scientists and drug discoverers in biotech history. 
  • Divesh Makan, General Partner, Iconiq Capital. Makan is credited with being one of the tech elite’s most trusted investors, and is one of the most savvy and influential investors in the artificial intelligence industry. 

And that’s not even half of the leaders that will join us for the extended conversation this year at DOC. We’re extremely proud of the community we’re building, and if you’re interested in joining us, please let us know! I hope to see at least some of you in Sonoma this Fall. 

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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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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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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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Running a startup means making hundreds of decisions a week – some of them simple – which brand of coffee to stock in the kitchen – and some of them impossibly complicated – should I replace my founding CTO? When parsing through them, I always consider a piece of advice a long-time colleague once gave me:

“There’s no value in an option destroyed.”

I can’t remember the first time he offered me that chestnut, but I’ve used it countless times over the past decade or so, and it’s always served me well. Take the CTO example, for instance. Let’s say he was a great early co-founder with a significant stake in the company, and he was central to the initial build of your product. He’s the only person at the company who truly understands your codebase. But as your company grew, he began disengaging – not responding to simple update requests, failing to come into the office for team meetings, ignoring the administrative and management duties inherent in running a growing team.

Then he does something that is clearly a fireable offense – perhaps he questions the company’s core mission in a public forum like Reddit.

You’ve already been thinking about parting ways with him, but now he’s practically asking you to do it. You’re pissed, your employees are confused, and your investors are asking what you’re going to do.

You have a few obvious options: First, you could immediately fire him. But that means you lose all the institutional memory your CTO has of how your product actually works. It also invites a painful confrontation and possibly even legal action.

Next, you could do nothing and try placate everyone. For conflict-averse founders, this is often the default, but it fails to address both the immediate and the long-term problem.

Maybe you could put him on a PIP and communicate to the team that you’re addressing the problem? But that requires he accept humiliation in front of the company, something he’s unlikely to do.

You only have bad choices. What to do?

It’s at this moment I run each choice through my colleague’s advice and ask myself: Which choice destroys optionality, and which leaves the most open? Running this math doesn’t predestine a decision, but it often clarifies a direction I’ll end up taking.

In this case, immediately firing the CTO means destroying the option of keeping his value to the company intact. He’s the only one who understands the code base – a vulnerability that you should have identified and addressed – but didn’t. You don’t want to close off the option of keeping him around – but by considering cutting ties with him through the lens of lost optionality, you’ve come to understand that while the CTO predicated this crisis, responsibility for its impact falls to you. That’s valuable insight that should inform your next move.

What options are destroyed by doing nothing? Initially this seems the safest choice – but that depends on how you define an option. Do you want to maintain your team’s confidence and trust? Do you want to be understood as a responsive and strong leader by investors or co-founders when it comes time to raise your next round or start your next company? You are likely damaging a future version of yourself by doing nothing – that’s an option destroyed. Another insight to inform your decision.

What about forcing your recalcitrant CTO to accept a PIP? Does that destroy any options? Possibly, because he might just quit, and you’re back to the problems of our first scenario, where you lose his institutional memory. Plus, PIPs rarely work for senior leadership – and the vibe in the company is most certainly going to be off for however long the PIP is dangling over your CTO’s head. And what happens if he doesn’t change his ways?

Our situation in a nutshell: You feel the need to take decisive action, because ignoring the CTO’s behavior damages your team and your own future options. But decisive action threatens to damage the company even more – if he leaves, you lose access to the core workings of your company’s technical infrastructure. Tough call.

Running the “options destroyed” exercise helps you understand the nuances of the problem, and perhaps an alternative approach comes to mind. No matter what, you’ll need to have an immediate, private sit down with your CTO. What will you say to him?

I’ve found the best approach is to lead with your personal disappointment, but also acknowledgement of  your role in how you both got to the present moment. Take responsibility for allowing the situation to get to the point where the CTO felt comfortable slagging the company mission, but force him to own the transgression of doing so. These kind of conversations are hard, but necessary, and they almost always lead to a breakthrough in terms of the next possible steps.

Your CTO owns a significant stake in the company, so his and your long term interests are aligned. Knowing this, your job is to identify and offer a path forward that allows him a way out with dignity. You need to announce a resolution of the issue to your stakeholders so the company can move forward. He needs a way out of a mess that he helped create.

It’s at this point in the thought experiment that you need to put yourself in his shoes – run the optionality exercise from his point of view. What is a solution  that best preserves his optionality?

Your job is to find a path that aligns his interests with your company’s – and to include him in that discovery. Perhaps you offer him an offramp – an advisory role and protection of a chunk of his equity vesting in exchange for an apology to the team and acknowledgment that he was ready to move on and should have realized it sooner. That both preserves your option to have access to his knowledge, and gives you time to find a suitable replacement.

Each situation is unique, but what presents as your initial choices are rarely the only ones. Asking yourself if you’re destroying future options for your company, your stakeholders, and yourself is a great way to explore possible alternatives.

A footnote: my colleague’s advice doesn’t just apply to decisions we make at work. It also applies to personal situations, as well as societal ones. In future posts, I’ll be exploring the idea of “options destroyed” as it applies to the choices we’re making – and not making – in our relationships to new technologies like AI, particularly when it comes to governance, policy, and economic flourishing.

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