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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Last night my wife looked up from her phone, disgusted. “All I’m getting is Jeffrey Epstein and Peter Attia!” she said. “Why do they think I’m interested in this?!”

As the family’s resident interpreter of digital entrails, I felt responsible to hazard an answer, but given the prurient nature of the Epstein story, I sensed my thoughts might not be well received. So I backed into it a bit: “Have you clicked on any Epstein-related links recently?” I asked. She had, she rejoined, wary of the implicit judgement hovering over my question. “But that doesn’t mean I want my entire feed to be about it!”

For whatever reason – and there are many, many possible reasons – the algorithms responsible for producing my wife’s feed had determined that the most likely content to perform *at that moment* were posts about Jeffrey Epstein and the longevity influencer Peter Attia. Did that please her? No. But was it explainable? I think so, and the conversation that ensued helped sharpen a hypothesis I’ve been considering for weeks: We’ve been living with at-scale versions of “generative AI” for a lot longer than we thought – and if we want to understand how generative AI might shape us going forward, it would pay to study the impacts its early forms have already had on our world.

You might wonder what I’m on about – and given I’m thinking out loud, it might help if we define a few terms. I asked Gemini for a short explanation of “generative AI.” Here’s what came back: “a type of artificial intelligence that creates new, original content—including text, images, code, music, and videos—by learning patterns from massive datasets.” Sounds about right.

I then prompted Gemini with this question: “how do feeds work on Instagram and TikTok – what drive the decisions the algorithms make?” Now, I’ve studied the answer to this question pretty closely over the past decade or so, and Gemini’s answer rang true to me: “Instagram and TikTok feeds use sophisticated machine learning algorithms to curate personalized content, aiming to maximize user engagement by analyzing thousands of signals, including watch time, likes, shares, and comments.”

If today’s generative AI delivers content by “learning patterns from massive datasets” and today’s social media feeds use AI to deliver content by “analyzing thousand of signals” to “curate personalized content,” well, it strikes me that social media feeds constitute something quite similar to generative AI, just delivered in a different product envelope. Instead of direct prompts, platforms like Insta, YouTube, and TikTok use our actions, our personal data, and thousands of other inputs to determine what we might see next on our feeds. In essence, the AI behind social media are generating our feeds on the fly, billions upon billions of times a day. It’s an insanely complicated (and rather out of control) process. And it’s no wonder that the companies behind those platforms – Meta, Google, ByteDance, et al – have come to dominate generative AI. It’s also no surprise that the newest entrant in the race – OpenAI – is trying to push its way into feed-driven social media. GenAI and social media are two sides of a very expensive coin, minted in a forge fueled by compute, cash, and at-scale data.

In short, social media – scaled, AI-driven content engines – catalyzed the revolution we now call generative AI. And what drives social media?

Advertising.

It’s no secret that advertising is coming to generative AI. OpenAI has already announced its plans, and Google quietly incorporated advertising in its “AI Overviews” over a year ago. Without the advertising industry’s massive revenue, AI providers will never be able to justify the hundreds of billions of dollars in investments they’ve already made in consumer-facing AI applications.

But a commitment to advertising comes a commitment to advertising’s imperatives – and we’ve seen exactly how those imperatives have played out through social media in the past decade or so. Will advertising impact future versions of generative AI in a similar fashion? That’s a question we should all be asking ourselves.  We may not like the answer, but there’s still time to imagine new models for how we engage with this new technology – and to demand more from the companies who provide it to us.

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It’s been fun to go back to Berkeley, where I first taught Journalism more than 20 years ago. I’m leading a seminar on how technology impacts journalism, with a particular focus on AI. The class asks students to read a bit of history – it’s hard to understand where we are if we don’t know how we got here. Search is a big part of that history, so I included a chapter of my first book – The Search – as a reading assignment.

As I prepared for class last week, I dug through my archives and unearthed The Search’s original manuscript. In the first chapter, “The Database of Intentions,” I opine on how search might lead to the development of AI that passes the Turing Test. Written 22 years ago, the passage anticipates the rise of generative AI. I start by drawing a distinction between data that is on our personal machines and data held in the cloud by large technology companies like Google. Then I think out loud a bit about where that all data might take us. Even though the writing is two decades old, it prompts some interesting questions about the moment in which we find ourselves.

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“It is somewhat awkward to say this as the CEO of an AI company, but I think the next tier of risk is actually AI companies themselves…the governance of AI companies deserves a lot of scrutiny.”

So much to say about this. But read the essay. Then we’ll talk.

Every so often I am asked to participate in a survey fielded by Elon University’s Center for Imagining the Digital Future. As you might expect, this year’s survey focuses on the impact of AI, and includes this prompt:

• If you do think it is likely that AI systems will begin to play a much more significant role in shaping our decisions, work and daily lives: How might individuals and societies embrace, resist and/or struggle with such transformative change? As opportunities and challenges arise due to the positive, neutral and negative ripple effects of digital change, what cognitive, emotional, social and ethical capacities must we cultivate to ensure effective resilience? What practices and resources will enable resilience? What actions must we take right now to reinforce human and systems resilience? What new vulnerabilities might arise and what new coping strategies are important to teach and nurture?

It’s rare that a survey asks its respondents to actually write something cogent and long form, so I figured I’d publish my response here. I’d be curious to hear your thoughts! If you’d like to participate, the link to the survey is here.

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The keys to engaging with and learning from information systems such as AI are similar to those we encountered with the rise of search (IE Google) and the broader world wide web. In short, we must prize the formation of high quality questions, and the ability to critically evaluate and take action based upon machine-generated responses to those questions. 

This statement presumes that society focuses on revising the approach of its academic institutions – particularly early schooling – with an eye toward teaching critical thinking, with a particular emphasis on the values that drive scientific methodology. In short, critical thinking becomes foundational in an age of AI. Those with highly developed sense of rational inquiry will prosper in the context of a world where ambient artificial intelligence exists. We already see this playing out, where the most fruitful applications of AI are found in medical, financial, and other research-intensive fields. 

Beyond critical thinking, another crucial action we must take is to intelligently regulate digital systems (AI-driven platforms in particular) to encourage a distributed architecture of power and control as it relates to data and ownership rights. The prevailing architecture in today’s commercial Internet cedes most power, control, and leverage over data to corporate interests (companies like Meta, Google, Apple, Amazon, Netflix, et al). Through complicated and opaque terms of service and related policies, these companies produce, store, and leverage consumer data in a centralized architecture that delivers digital services back to the edge, but retains power and control at the center. A central question of the AI era will become whether power and control will migrate to the edge. 

Another way of thinking about this issue is by asking this question: Who does the AI ultimately work for? Is it controlled by the end user, or is the AI ultimately controlled by a centralized platform like OpenAI, Google, or Meta? 

The “surveillance capitalism” model developed over the past 25 years of Internet history is currently shaping the business and product decisions of AI-first companies. Whether that model continues to prevail will have immense implications on the kind of society we live in 5-10 years from now. Regulatory frameworks which encourage the movement of data provenance and ownership rights to the edge of the network – to users – could unleash exponential innovation and flourishing in our economy. But maintenance of the status quo will concentrate power and profit in the hands of the few, portending significant societal rupture in the future.

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Google launched as a free public beta in the Fall of 1998. It was a revelation – a 10X improvement on Internet navigation and research. But from its launch forward, Google’s founders were hounded with questions as to how their company planned on actually making money. John Doerr, one of Google’s earliest backers, famously answered that question by citing Google’s extraordinary growth: With all that traffic, he said, we’ll figure it out.

Google’s founders were famously suspicious of advertising – in their white paper explaining Google’s PageRank technology, Larry Page and Sergey Brin argued that advertising-funded search engines would be “inherently biased towards the advertisers and away from the needs of consumers.”

Two years later, Google launched AdWords, which became a multi-hundred-billion dollar advertising business over the following two decades.

We all knew that OpenAI was going to follow Google’s path into advertising, and late last week the company made it official. There is *a lot* to unpack around this announcement, but it’ll have to wait: I’m starting a new gig this week, teaching a seminar on the impact of technology at the UC Berkeley Graduate School of Journalism. I’ll be spending a fair bit of time in my former home of Northern California, and once I get settled, I’ll return to a more regular posting schedule here. In the meantime, if you find yourself in NorCal and you’d like to reconnect, let me know!

It took me two weeks, 6000 words and nine posts, but I can finally round up my predictions for 2026 in one place. Here’s the complete list in one handy, blissfully shortened post. Thanks for reading, and once (and for good), I wish you a happy, healthy New Year.

#10 – The Feed Declines (Predictions 2026, #10)

The algorithmically induced sugar sludge that has dominated culture for more than ten years is failing, and 2026 will be the year most of us notice that trend. And the company that will be most impacted? Meta and its flagship Instagram app.

#9 – Anthropic Goes Public (Predictions 2026 #9)

Anthropic has a cleaner structure, cap table, and set of relationships than OpenAI, which should allow it to be more nimble in its journey to becoming a public company. In addition, I sense Anthropic’s corporate culture would embrace the responsibilities and reporting requirements inherent to being a public company. Anthropic also has a better financial story to tell – it’s been focused from day one on the enterprise market, where it is a leader.

#8 – AI Can’t Cost This Much (Predictions 2026, #8)

2026 will be a year of innovation when it comes to the cost of compute, as well in how much compute is actually needed to perform the magic we’ve come to expect from AI applications. It’s happened over and over again in this industry, and I think pricing the future based on the cost of the present is a losing bet.

#7 – The Year Tech Gets Even Bigger (Predictions 2026, #7)

For the past five or so years, tech giants have had to play defense when it comes to M&A and sweetheart partnerships – Meta was being sued over its acquisitions of Instagram and WhatsApp, Google over its consolidation of adtech and its domination of search distribution through deals with Apple and Samsung, among others. But in 2026, the governors are coming off.

#6 – Finally, Voice Interfaces For The Home (Predictions 2026, #6)

By year’s end, we’ll have ambient AI in our homes, and it will actually work as expected. This in turn will shift what we expect as consumers of technology, in our cars, on our phones, and in the world around us. It won’t be a revolution, but when we look back at 2026 ten years from now, we’ll realize that this was the year “ambient intelligence” took off.

#5 and #4 – Health Takes Center Stage, Open Evidence Acquired (Predictions 2026, #5 and #4)

2026 will be the year that health takes center stage in the societal debate around AI. And OpenEvidence will be acquired by either OpenAI, Google, Apple, Microsoft or another advertising-driven big tech player (let’s not forget that Microsoft owns LinkedIn). In 2026, everyone will have a point of view on how health and AI interact.

#3 – Magic and Mayhem (Predictions 2026, #3)

This will be the year that AI moves beyond chatbots and into the fabric of everyday life in ways that will surprise and delight hundreds of millions of people, changing what they thought they could accomplish and reordering economic productivity along the way. That in turn will drive tectonic shifts in the business models underpinning most companies reliant on digital technology. There will be lots of magic this year. But there will also be plenty of carnage as previously unbreachable moats start to crumble, not only in business, but also in society at large.

#2 – Battle Lines Are Drawn (Predictions 2026, #2)

This year, ideological battle lines will be drawn – you’re either in favor of the glorious future that AI promises, or you’re fighting the plutocrats seeking to cement their power through mechanisms of surveillance capitalism. The evolution of society with AI will be messy, ungoverned, and seemingly incomprehensible. But at its core lies an existential question: What happens to us when we build machines capable of fabricating reality, seemingly rendering us obsolete in the process?

#1 – Do You Trust The Conjurer? (Predictions 2026, #1)

In 2026, we’ll collectively decide if we trust generative AI, and any number of actors will prosper or fail based on that trust.  Apple will leapfrog into the top echelon of AI companies, OpenAI will struggle, and large consumer brands will realize that in the context of AI sweeping through society, their most effective competitive advantage is the trust their brands evoke amongst consumers. 2026 will be the year that trust becomes the essential ingredient in business, culture, and society. And we’ll have the conjuration of generative AI to thank for the trend.

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Previous predictions:

Predictions 2025

2025: How I Did

Predictions 2024

2024: How I Did

Predictions 2023

2023: How I Did

Predictions 2022

2022: How I Did

Predictions 2021

Predictions 21: How I Did

Predictions 2020

2020: How I Did

Predictions 2019

2019: How I did

Predictions 2018

2018: How I Did

Predictions 2017

2017: How I Did

Predictions 2016

2016: How I Did

Predictions 2015

2015: How I Did

Predictions 2014

2014: How I Did

Predictions 2013

2013: How I Did

Predictions 2012

2012: How I Did

Predictions 2011

2011: How I Did

Predictions 2010

2010: How I Did

2009 Predictions

2009 How I Did

2008 Predictions

2008 How I Did

2007 Predictions

2007 How I Did

2006 Predictions

2006 How I Did

2005 Predictions

2005 How I Did

2004 Predictions

2004 How I Did