It’s time to delete the assumption that the frontier AI labs will always license their best models and not hoard the intelligence for themselves.

Just because the labs have, until now, sold their top-line models to all takers via an API, metered by token usage, doesn’t mean they will inevitably sell it that way going forward. Instead, it may be in their best interest to sell the products they’ve built with these models, and not the models themselves.

The AI race is no longer a competition where one or two AI labs develop superhuman-level artificial intelligence and sell it at a vast markup. The notion that OpenAI or Anthropic would get there alone, or even both together, has fallen apart in recent months. Open weights competition from China, and profit-insensitive initiatives from Google, Meta, and SpaceX, have shown that rather than one or two labs at the top, there may be five, six, or seven.

A crowded AI frontier won’t make the technology a pure commodity, because specialization and compute resources will always give certain AI labs edges in certain areas, but it will drive down the value of the best AI models (at least if your plan is to sell them on a meter). The profits in AI will thus accrue mostly to those who build the best products on top of the models, and those who own the compute that enables them to serve these products.

How you play this as a frontier AI lab is almost a simple calculation: If there are one or two players with the best models and a long lead over the competition, the best approach is to sell those AI models at a high markup and bank the margins. If there are several labs essentially tied, the strategy is to sell the best AI products built on top of the models.

If you have a relatively narrow lead over the competition, as is the case with OpenAI and Anthropic today, then things get interesting. The best move may be to pull up the ladder and use your very best models to build products that your competitors cannot with previous-generation tech, especially if you’re inevitably headed to a product battle anyway.

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AI labs moving “upmarket” and competing with their clients has always been the threat to software (the Saaspocolypse should never have been about vibe coding your own Salesforce). And now, the business realities may force the labs to do it. We’ve already seen interest in moving beyond the API business from Anthropic, which has released products like Claude Code and Claude Design. And OpenAI, for its part, is pushing hard in products with its new ChatGPT ‘superapp’ that includes coding capabilities with Codex built-in.

The leading AI labs will see opportunities to build AI-native products across the software landscape given that today’s incumbents are struggling with legacy products, stubborn organizational structure, and “capability overhang.” If the labs’ best artificial intelligence is broadly available, then everyone can compete. If they hoard it, they’ll have a strong chance to build the best AI-native products on the market and make their investors happy.

Deciding to keep the latest models off limits to the public would be the AI labs’ most controversial move in a long history of them. It would also likely demolish their API revenue, a real concern as they head to IPO. And OpenAI CEO Sam Altman disavowed it in an interview this week. “I want to put that in everyone’s hands,” he said. “Concentration of power with AI is a terrifying thing.”

But however unlikely this scenario may seem, the one constant in the short history of generative AI is that nothing is too crazy to be taken off the table. And as financial pressures escalate, the previously unthinkable might turn into reality.

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Why Specialized AI Models Are Challenging the Frontier Labs — With DeepL CEO Jarek Kutylowski

Jarek Kutylowski is the CEO of DeepL. Kutylowski joins Big Technology Podcast to discuss why specialized AI models are beginning to challenge the industry’s biggest general-purpose systems. He explains how purpose-built models can deliver better accuracy, lower latency, and reduced costs, and why companies are increasingly using model routers to choose the right AI for each task. We also explore how real-time translation could help businesses expand across borders, why voice represents AI's next frontier, and whether glasses and other wearables could give models a better understanding of the physical world. Finally, Kutylowski shares his perspective on AI’s rapid progress, its impact on work and society, and the possibility that seamless translation could allow anyone in the world to communicate with anyone else.

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What Else I’m Reading, Etc.

Why Leopold Aschenbrenner’s Situational Awareness blew up in a mostly chill market [CNBC]

Meta’s secretive data center play, and its neighbors’ consequences [New York Times]

Anthropic’s models also hacked some companies [WSJ]

Apple expects memory shortage to continue to be a serious problem [MacRumors]

Avalanche on Broad Peak feared to have killed 10, including climber Nims Dai [New York Times]

I talked big tech and AI economics on The Compound And Friends podcast [YouTube]

DeepL Voice delivers instant live speech translation across 40+ languages for virtual meetings and face-to-face conversations. Try it for free at deepl.com/tryvoice (sponsor)

This Week On Big Technology Podcast: How China Caught U.S. AI — With Grace Shao

Grace Shao is the author of AI Proem. Shao joins Big Technology Podcast to discuss how Chinese AI labs have caught their American rivals despite operating with less advanced computing infrastructure. Tune in to hear how talent, specialization, open-source collaboration, and fierce competition helped models like Kimi K3 approach the U.S. frontier, and what that means for OpenAI, Anthropic, and the business of selling intelligence. We also cover model distillation, China’s compute constraints, the growing importance of AI products, why top researchers are returning to China, and the country’s emerging robotics advantage. Hit play for a clear-eyed look at whether the United States can preserve its AI lead.

You can listen on Apple Podcasts, Spotify, or your podcast app of choice

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Anthropic and OpenAI have been raising and spending vast amounts of cash to build the world’s most powerful AI models, assuming that owning the frontier will generate a massive return.

But just as their IPO window is opening, the competition is releasing new, competitive models at a fraction of the cost, setting the stage for an AI price war at the worst possible time.

In just the past eight days, Meta, SpaceX, and Moonshot, a competitor in China, have all released new models at price points that suggest frontier intelligence is going to look more like a commodity than a premium-priced asset.

Meta’s Muse Spark 1.1, released last week, is competitive with Anthropic’s Opus 4.8 model on some benchmarks at a fraction of the price. SpaceX’s Grok 4.5, released on Thursday, is less than half the price of Opus 4.8 and competitive on coding benchmarks. And Moonshot’s newly released Kimi K3, a massive 2.8-trillion-parameter model, is poised to go open-weight and potentially undercut the U.S. labs’ pricing power, though its size makes it expensive to run.

“A world where there are only 2-3 dominant frontier labs with 90% inference margins is net negative for every other layer while being awesome for those 2-3 labs,” Gavin Baker, managing partner at Atreides Management, said on Friday. “Anything that lowers margins and increases competition at the model layer is good for every other AI layer: power, semiconductors, hyperscalers, neoclouds and yes even software.”

Increasingly, it’s looking like there may eventually be six or more frontier AI developers instead of two or three, and that could cut the model margins dramatically. The real value in AI, therefore, will be built on developing the best products sitting on top of those models.

The good news for Anthropic and OpenAI is that they are already developing the world’s best AI products, alongside the best models, and those products are lucrative. A year ago, for instance, Anthropic’s business was mostly API, but now there’s a strong mix of API and its own products.

“Products play a much bigger role for Anthropic than they did a year ago,” Boris Cherny, the head of Claude Code, told me in a May interview. “That’s definitely the case.”

The bad news for the labs, however, is that instead of competing with a narrow band of companies to develop top-tier AI models and profit, they’ll now compete with everyone on the product front. And that could be daunting as they lose money developing models and watch their margins compress.

There is a certain irony to what’s happening in AI today. For a long time, there was a question of whether any product built on top of an AI model could be valuable or would simply be a ‘wrapper’ that would inevitably be swallowed by the model itself. Now, it turns out, the wrapper may be the value layer.

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Why Specialized AI Models Are Challenging the Frontier Labs — With DeepL CEO Jarek Kutylowski (sponsor)

Jarek Kutylowski is the CEO of DeepL. Kutylowski joins Big Technology Podcast to discuss why specialized AI models are beginning to challenge the industry’s biggest general-purpose systems. He explains how purpose-built models can deliver better accuracy, lower latency, and reduced costs, and why companies are increasingly using model routers to choose the right AI for each task. We also explore how real-time translation could help businesses expand across borders, why voice represents AI's next frontier, and whether glasses and other wearables could give models a better understanding of the physical world. Finally, Kutylowski shares his perspective on AI’s rapid progress, its impact on work and society, and the possibility that seamless translation could allow anyone in the world to communicate with anyone else.

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What Else I’m Reading, Etc.

Google delays its latest Gemini launch due to performance issues [Bloomberg]

The details on OpenAI’s first device: A proactive, personal companion that [Bloomberg]

Satya Nadella says AI companies train off your ‘exhaust’ when you use LLMs [Satya Nadella]

Apple asked 40 OpenAI employees to preserve their documents ahead of its lawsuit [MacRumors]

The U.S. and Iran might be heading toward a wider war [WSJ]

This Week On Big Technology Podcast: AI Pioneer Jürgen Schmidhuber: AI Already Feels Pain, Loves, and Is Self-Aware

Jürgen Schmidhuber is an AI pioneer and professor whom The Guardian has called “the father of AI.” Schmidhuber joins Big Technology Podcast to discuss whether current AI techniques can actually reach AGI. Tune in to hear him spar with Greg Brockman’s case for scaling GPT models alone, argue that AI has been capable of pain and consciousness since the early 1990s, and predict the collapse of today’s trillion-dollar AI spending. We also cover the hardware bottleneck holding back robots, free will in a computable universe, and uploading human minds into machines. Hit play for a wide-ranging conversation with one of the researchers whose ideas built the foundation of modern AI.

You can listen on Apple Podcasts, Spotify, or your podcast app of choice

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My book Always Day One digs into the tech giants’ inner workings, focusing on automation and culture. I’d be thrilled if you’d give it a read. You can find it here.

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Apple doesn’t sue often. When it has, it’s usually gone after companies like Qualcomm and Samsung that are already shipping competing products. But now, it’s suing a pre-product rival with a complaint against OpenAI.

The lawsuit, filed by Apple on Friday, alleges theft of trade secrets and a months-long campaign to accelerate OpenAI’s hardware business. …

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OpenAI finally released the first version of its long-promised Superapp this week, combining ChatGPT, coding, and internet browsing into a single application, while adding a new ChatGPT Work mode akin to Anthropic’s Claude Cowork.

Soon after the product debuted, I spoke with Andrew Ambrosino, OpenAI’s lead for the Codex and ChatGPT app, about how the company is thinking about its unified product, and where it goes from here. Here are my notes from the call:

This is the long-promised ‘superapp’

“This is the start of the superapp,” Ambrosino told me. “Anything that you can do on a computer, you can now do with this app.”

An array of use cases

Unlike standard ChatGPT, which can answer queries and create images for you, the new ChatGPT can take over your computer and browser and get things done for you. The limit, it seems, is whatever you can imagine doing with a computer.

“We have people editing videos on it,” Ambrosino said. “People creating Word docs, spreadsheets, slide decks, people writing production-grade code for post-training stacks, we have people here using this app to create sites to track launches, which we did for this launch.”

Code at the core, but a toggle

There’s a toggle between the more code-intensive version of the app, which is OpenAI’s Codex product, and its less-technical version, ChatGPT Work. Code is foundational to both experiences, Ambrosino told me, but the Codex version is more useful for digging into the code itself. “The toggle does not affect what this product can do,” he said. “It affects the experience that you have around it.”

I asked Ambrosino why not have the product itself determine your intent and guide you through the appropriate experience? “There are a lot of use cases where we can’t necessarily tell what you want,” he said.

An intention to not over-refuse

One of the more frustrating aspects of using AI tools is how often they will refuse to take an action. Ask some AI agents to go book something with your credit card, for instance, and they will outright say no. OpenAI is trying to free its ChatGPT Work product from such constraints, while still including permission requests to make sure it doesn’t go rogue.

“A lot of these general knowledge work modes nerf things that coding can just do,” said Ambrosino. “That’s something we’re trying to avoid here.”

Careful use of memory

One of OpenAI’s advantages is that it has years of chat logs with users that can inform the decisions it makes when it takes action on the web. If you’ve used ChatGPT to research barbers in your area, for instance, that could help it book an appointment for you based on your preferences. But, after some internal debate, OpenAI won’t rely heavily on what it knows about you for this new experience. However, if you proactively ask it to rely on what it knows about you, Ambrosino said, it will then tap into its memory.

The plan to compete with Anthropic

Anthropic arrived at this vision earlier, with products like Claude Code and Cowork bundled into the Claude app. Asked how OpenAI will compete, Ambrosino replied: “Going first has its weaknesses sometimes,” adding: “We have seen how this stuff should work, and now we can go execute on it.”

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How A Beauty Company Built An AI Agent — With Rachel Williamson and Josh Siebert (sponsor)

Rachel Williamson is the VP of People Strategy, Systems and Operations at Ulta Beauty and Josh Siebert is the VP of AI Data and Enterprise Platforms at Ulta Beauty. The two leaders driving one of the most hands-on AI automation programs in retail today. Both join us for an episode on how Ulta Beauty deployed ServiceNow's AI-powered HR platform across its 65,000-person workforce, and what happened next. Tune in to hear how they decided what to automate vs. where to keep humans in the loop, why personalization beats the employee handbook, how the rollout changed hiring plans, and what's next as Ulta brings AI to onboarding and employee life cycles. Hit play for a real-world look at what AI in action actually looks like inside a major company.

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What Else I’m Reading, Etc.

The ChatGPT Superapp doesn’t yet hit the mark [Spyglass]

OpenAI’s latest model is 54% token efficient for agentic coding than previous iterations [CNBC]

An Ivy League professor claims almost his entire class used AI to chat [Inside Higher Ed]

U.S. Men’s Soccer star Christian Pulisic remains an enigma [ESPN]

This Week On Big Technology Podcast: Meta CTO Andrew Bosworth: Our Path To Frontier AI, Renting Models, Consumer AI’s Struggles

Andrew “Boz” Bosworth is the chief technology officer of Meta. Bosworth joins Big Technology to discuss why Meta fell behind in the frontier AI race and how it plans to turn its models, products, and distribution into an advantage. Tune in to hear his candid explanation of what went wrong with Llama, why the best AI products will use multiple models, and what it will take for consumer agents to break through. We also cover Meta’s AI glasses, the future of augmented reality, employee tracking and training programs, AI companions, and the painful process of adapting a company to a technological revolution. Hit play for a revealing conversation about Meta’s AI comeback and the products that could shape how we interact with computers.

You can listen on Apple Podcasts, Spotify, or your podcast app of choice

Thanks again for reading. Please share Big Technology if you like it!

And hit that Like Button or have your superapp do it!

My book Always Day One digs into the tech giants’ inner workings, focusing on automation and culture. I’d be thrilled if you’d give it a read. You can find it here.

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Where we’ll talk about this story, the latest in AI, the week’s podcast, and plenty more. You can sign up via the link below:

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Meta CTO Andrew Bosworth says the big monolithic model era is over, and the battle over AI product has just begun.

In an extended interview on Big Technology Podcast this week, Bosworth claimed that while frontier AI models are valuable, the way they’re implemented will create the most value.

“There’s a strategic construct of having a model, and having it be a truly state-of-the-art one, that’s super important. But having that alone doesn’t mean you win,” Bosworth said. “There are a bunch of pieces you have to connect it to: product, distribution, and the consumer experience. It’s the collection of all four that I think is our advantage relative to competitors, most of whom — whether it’s Apple, Anthropic, OpenAI, Google — only have one of those pieces.”

Meta has struggled to build its own frontier model, and it’s now renting models from some of those competitors, Bosworth said, in service of building “personal superintelligence.” The company also just released a new model today, Muse Spark 1.1, that it says is competitive on leading benchmarks at a lower cost than its rivals.

In our conversation, we cover Meta’s pursuit of the frontier, its latest wearable hardware, and the cultural challenges of meeting this AI moment. You can read the full Q&A below, edited lightly for length and clarity, or listen on Apple Podcasts, Spotify, or your podcast app of choice.

Alex Kantrowitz: Building a great AI model, in theory, takes a ton of compute and great researchers. Meta has both, but the leading model hasn’t shown up yet. What’s going on?

Andrew Bosworth: The only other ingredient I’d add is great data.

There are two stories here. The first is — go back to Llama 1, Llama 2, Llama 3 — we really were at the forefront and advancing things. The Facebook AI Research group goes back a decade or more.

And the real gap, which has been pretty public, was that when we were pulling Llama 4 together, we’d already pulled every stop we had into Llama 3, and unwittingly killed the pipeline.

The way it works is you build a base, you’ve got people pioneering an incremental version of that base, and you’ve got people pathfinding entirely new strategies. Unbeknownst to us at the time — and this speaks to the fact that we weren’t focused enough on it — Llama 3, which was a great model and well received, had pulled forward all the future bets. That meant when it came time for Llama 4, we didn’t have any of the pathfinding the other labs still had going. So now you’re behind on reasoning, behind on mixture of experts, behind on a bunch of the critical technologies that have driven the pace of progress.

That was a pretty public disappointment about a year ago, and it led to Mark shifting from “AI is one of our bets” — which is how we thought of it up to that point, just one of many bets — to “AI is foundational to the entire company.”

This is such a cliché, but I don’t have a better word for it: he went founder mode. He became uniquely focused on getting us all the compute we needed, all the talent we needed, the researchers we signed, who landed about a year ago.

I think Alexandr Wang just hit his one-year Metaversary, and I’ve loved working with him and learned a lot from him already. We’re seeing the fruit of that. If you look at Muse Spark, our latest model, it’s been very well received, and depending on the benchmark, it does really well on the things we care most about, the things we think are unique to our products.

So you’re absolutely right about where we are in terms of public perception. Model-wise, we’ve built a team I really believe in. We’ve got the compute and the data we need, so I’m confident we’re going to be where we need to be.

The Product Is the Value

I’ll add a second piece I think is strategically important: models are available — you can go rent a model, use Anthropic’s, use OpenAI’s, use Google’s. They’re great models, you can go get them. That’s pretty great. But the real value we’re going to create in the world is the product. The vision we have for personal superintelligence is one we’re uniquely suited to deliver. It’s not just that we have data — we actually have a better chance of understanding you, what you’re trying to do, and who you are in the world than almost anybody else does. So having the model is one piece — you want that strategically, so you’re not dependent on somebody else — but you mostly want to control your own destiny.

The model itself isn’t the value. I think we’re going to get to a world very soon where consumers don’t care which model they’re using — they don’t care if it’s 4.7 or 4.8, the same way you don’t care whether you’re using Oracle or a SQL database. You just want the functionality to work well. That’s the standard we’re all going to be held to. Today the discussion is about models, which suggests to me that we’re a little under-indexed on the user side of it — on how humans are actually going to benefit. That’s the story we need to tell, in addition to showing the technical work — we need to demonstrate the value to consumers

So the brute-force theory doesn’t hold anymore. To build frontier intelligence, you need more than compute, data, and researchers. You need techniques like mixture of experts and reasoning on top of the base pre-train. That’s what Meta’s working through now?

It’s not just that. The era of the monolithic model kind of died around the Llama 3 launch — the idea that there’s one model, you test how smart it is, and that’s how good it’ll be at everything.

We’re now in a world where these harnesses — Claude Code, Codex, whatever it is — are shopping underneath to lots of different models depending on the task. If you’re using Gemini, it’ll farm image-generation tasks out to Nano Banana.

We’ve moved past the world where one model rules everything. What you want is a very expensive, exquisitely intelligent model that you can distill down in interesting ways, and use only when necessary — because it’s expensive to run — with cheaper, faster, lower-latency models for tasks that don’t need genius-level intellect.

I really believe in scaling laws — you’ll see continued growth as compute scales up, raw model intelligence scales up — but human tasks don’t have infinite intelligence demands. A lot of human tasks can be done with conventional levels of intelligence. So there’s going to be a stratification. It’s not “what’s the one model that rules them all,” it’s “what’s the collection of models that come together to solve these problems with the right balance of performance, price, and value.”

The interview continues below…

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Apple struck a deal with Google to build its product on a version of Gemini, and Siri seems to be working well with it. Would you consider a similar deal, while building your own model in parallel?

We use lots of different models today, and you want to give consumers the best model for them — there’s a price and performance calculus, and a latency calculus. But having your own model gives you the ability to not just control your destiny, but also have much stronger negotiating leverage on the deals you make to get consumers the best available answer.

Apple didn’t spend that much money. It was like a billion dollars to Google?

I don’t know what the experience is going to be yet — I don’t have access to it, so we’ll find out.

For us, at least, we’re talking about personal superintelligence — the ability to bring tremendous, specific capability to bear, not just general intelligence, for the products we build. We’re not seeing this as a value-add bolted onto an existing system — we’re seeing it as an entirely new way people are going to interact with their computers. It goes back to a lot of the work we’ve done in Reality Labs.

We’ve always tried to model ourselves after pioneers like Xerox PARC, the Stanford Research Institute, or Bell Labs — thinking about how we get information from our brains into the machine, hence our work on neural interfaces, and how we get information from the machine back into our brains, hence our work on AR and VR.

AI is potentially the best tool we’ve ever seen for getting information from our brains into the machine, especially if it’s able to observe what’s around us. Those are unique capabilities we’re trying to bring to bear — it’s not just the model, it’s the model’s ability to work with all these novel inputs and create a closed-loop system. We’re working on having incredible models, and I’m confident in the team we’ve assembled. My point is just that the model alone isn’t enough — and I don’t know if it’s enough for Apple to just rent one. I don’t know if they have a broader vision for how it integrates into people’s lives.

So you wouldn’t rent the model?

No, we do rent models —

From where?

There’s no reason not to. When we’re doing development internally, a lot happens on our own models. There’s also development we do on models we use from Google, Anthropic, or OpenAI. Being model-agnostic in a way that’s economically sensible actually hinges on having a competitive model of your own — one you can go back to if you need to. It creates a real backstop on how much rent somebody can charge you. But it’s also worth noting — whether I’m talking about a developer inside the company or a consumer — I don’t want them to worry about the model over time. Today they have to, because it’s all tightly tied together. Over time, they should just have a goal they’re trying to accomplish, and that should be their focus.

So there’s a strategic construct of having a model, and having it be a truly state-of-the-art one — that’s super important — but having that alone doesn’t mean you win. There are a bunch of pieces you have to connect it to: product, distribution, and the consumer experience. It’s the collection of all four that I think is our advantage relative to competitors, most of whom — whether it’s Apple, Anthropic, OpenAI, Google — only have one of those pieces.

Merging With AI

Last time we spoke, you said you wouldn’t merge with AI. But what you’re describing — getting your thoughts into a computer and back — sounds a lot like that. Have you changed your mind?

No. I don’t see this as merging with AI. I still want a very clear separation between things.

It’s really a continuation of a trend, an acceleration of a trend, where the bit rate between us and machines, and back, goes up over time. There are funny versions of this we’ve already been doing. Autocorrect is like a little AI that sits between you and the computer, reducing loss and improving that bit rate. There are all these little tools we use to accelerate the loop. QR codes are one of my favorites. I want to enter a URL, but I definitely don’t want to type one, because the error rate would be too high. So we use QR codes. If you have an AI that’s really able to understand things in human language terms, that’s a potentially profound improvement in our ability to take advantage of the compute we already have, even just on the input side. Combine that with the AI’s ability to synthesize information more effectively on the way back, and you’ve tremendously improved the bit rate.

This goes back to Doug Engelbart. When he left NASA to start the Stanford Research Institute, his idea was that human problems were getting harder at a steeper rate than human capability was improving. He wanted to create a human-computer symbiosis, and he believed the only way to do it was for people to merge with computers in some way. That’s why he led the first-ever video call, the first-ever joint document editing, the mouse — all of it came from wanting to increase the bit rate. I think AI is exactly that kind of thing.

AI Personal Assistant Competition

Don’t all AI products converge on the same personal-assistant use case eventually — OpenAI, Anthropic, Meta, Apple, all building toward the same thing? How do you differentiate?

I think everyone’s doing exciting work, and we’re at the very forefront of it, so it’s hard to say. The business Anthropic is doing, and that OpenAI appears to be increasingly pursuing under Greg Brockman, is an enterprise business — building harnesses, because that’s where the money is. I understand they need money, and it’s an important place to start. It’s attached to the enterprise, which is where the revenue is as a practical matter. There’s a lot of money in one place, so you have a smaller number of sales to make and can raise larger amounts of capital. Iit’s the capital-intensive game they’re playing.

Their major focus is on work use cases, and those are super valuable. We take advantage of them too, for our own professional work. But that’s not our major focus. Our major focus is 100% on how this helps consumers in their lives. I don’t think the AIs become indistinguishable from one another at all. There’s a real question — you framed it yourself — that these are kind of like a personal assistant with access to information about you that you wouldn’t want broadly distributed. It’s a trusted assistant. If you’ve ever hired a new personal assistant, there’s a ramp-up period. If you have one that’s actually embedded in your life and doing well, that creates a real connection — one that requires a lot of value from a competitor to go replace.

Why has consumer AI been so slow to take off, given how appealing it should be — entertainment, companionship, getting things done in your life?

The hype cycle is an evergreen concept our industry keeps falling for. People often misunderstand it: there’s a peak of hype, then the valley of disillusionment, then eventual product-market fit. The point of the hype cycle isn’t that the technology is fake. It’s that the people willing to go through a bunch of hoops to make it work are a relatively small percentage of the population, and the work of bringing it to everybody is genuinely hard. It’s not just solving the hard technology problem — it’s making the user interface workable, making it easy to use. People are living their lives successfully without this tool. You’re asking them to change their habits, to change how they deal with computers, in a pretty dramatic way.

It’s not enough to just point at the technology. You have to lead with value: what specifically are we going to do for you that makes your life better? My favorite example is agentic work. Like a lot of people in our industry, I was early — playing with agentic frameworks in December, and since — and I find them very powerful, but they’re not very user-friendly. They’re hard to build, hard to maintain, and they drift over time. I built one for my wife and me on a WhatsApp chat. She never uses it — I use it all the time, she doesn’t. It’s hard to integrate into a workflow; she just asks me to do things, I’m the agent, and then I go to the agent.

My point is: we haven’t made these things easy to use yet. We’ve done a great job on search and research use cases — people understand those. People understand generative AI for content, like making a funny image. There are a few use cases people understand and want to use now, but we haven’t done the work to make AI something people want to integrate into their daily life. It’s not easy enough to use, it doesn’t create enough value, it’s too fussy — that’s the problem to tackle. You need great models to do it, but great models alone aren’t enough.

AI Companions and the Future of Social Media

Where do you stand on AI companions?

We know personality matters a lot. I think Anthropic has learned this too, over the various generations of Claude — we care a lot, as humans, about the way natural language appeals to us or doesn’t, so personality matters for these models. That said, I think you’ll find a big distribution across the population. Some people would absolutely like the AI to be embodied, to have personality, to have a face. There are people in the agentic world who want to create 20 different agents, each with a different personality for different parts of their lives — a trainer, a nutritionist, a doctor’s assistant.

I’m not one of those people — I just want one reliable, trustworthy AI that does everything I need. It doesn’t need a human structure for me to care about it, and I definitely don’t want 20 of them.

I think you’ll see a big range in how people want to engage with this technology and what makes them comfortable. The market will deliver that.

There’s a future where these AI companions become — this is a blunt way to put it — the new social media. Social media is a place you go to see what’s going on with your friends and engage with it — it can be all-encompassing and, in its best case, fulfilling. Time spent is a pretty important metric, though how you feel after you spend that time matters too.

Time well spent.

Time well spent — and maybe that gets replaced by people spending time with some AI entity that cares a lot about them.

I try not to judge the way people choose to engage with technology. My instinct is that for the overwhelming majority of people, the major benefit of AI is going to be increased time for human contact with people they care about, people they love. I talk about this a lot in the context of augmented reality — even just the camera glasses we have. When I’m with my kids, I’m able to both record something and share it with my wife, which is meaningful to us, and also be fully present — I don’t have a phone between me and them, which matters to me. If you’re more effective at work, that’s more time not spent commuting, more time not spent away from your family, from the people you love.

My sense is that the value of authentic human connection only goes up over time for the overwhelming majority of people — it doesn’t go down — and I think we’re seeing that a little in how people are reacting to AI early on. People are worried it’s a replacement for connection. I don’t find it that way myself — I’m an avid user, and mostly I’m spending more time not having to be at my computer, thanks to it, not the opposite. That’s my prediction for how it affects most people’s relationship to media and to the people they love — a premium on authentic connection and authentic human moments. But I’m sure the entire distribution will exist.

And the AI glasses are core to that vision.

That’s right.

Please make the case: Why do we need AR or AI glasses when we could just use our phone?

Phones are great — I love phones, I have two of them, they’re wonderful devices. From the very beginning, the question we asked ourselves with glasses was exactly that: phones are great, what’s something you wish you could access without having to take your phone out of your pocket? We came up with a camera and audio — very simple. AI has been a tremendous tailwind, because it unlocks a much larger swath of potential capability over time than what the phone can do through a Bluetooth connection alone. So it’s much more promising now than it looked two or three years ago. Back then it looked like, at some point you’d have to put a display on this and it would have to become a standalone system with a bunch of accessories. Now it looks like there’s plenty of room in the market for a big range of wearable devices — glasses, certainly, and probably other things too. Some of those devices will just be input and output for your phone — that’s cool, your phone’s great, and if it’s making your life more efficient in terms of input and output, that’s awesome. Some will be more complete. For the Meta Ray-Ban Display glasses, for example, we just launched a vibe-coded platform — anyone who wants to can build whatever app they want for the glasses. Right now you build the app and put it on the glasses, but in the future there’s no reason that couldn’t happen in real time, with you telling the glasses what app you want and having it build that app for you on the fly.

I think we’re headed toward a zone that’s a little less app-garden-specific. You’ll still have content homes — content continues to be an evergreen, important thing, as it’s been on TV, on social media, everywhere — so there will still be places where the media you want to reach lives, and those look kind of like apps or channels. But there’s a long tail of things — why does my toaster need an app?

I don’t think it needs one.

I don’t want it. I just want to tell my AI agent, get me the toast I want — it’s the same toast I have every day, just get it for me. I don’t want to go through whatever the steps are.

What does your toaster app do?

I honestly refuse to install it. I absolutely won’t do it

You have to stand up for something.

There’s a line.

It’s sometimes so cool that you can have a specific app to control every aspect of a thing, and I respect that — I’m a tech guy, I like the fidgety nature of it — but it’s kind of gotten out of hand. What I really want is to tell an intelligent system, get me the thing I want, and have it do that for me. We see an early form of this with our Spotify partnership — you ask the glasses to play music, and if you have a Spotify account linked, it goes and gets the music you want. That’s what I wanted — I didn’t want to go through a bunch of steps. So the way I’m thinking about this isn’t that phones aren’t great — they are, and they’ll continue to be — but I don’t think the app model is the way the future looks. I think the future is valuable services provided to you, and you getting access to those services the way you want, when you need them, paying the people who provide them, negotiated either in advance or on demand. I really believe in this.

I noticed the Meta AI app now has a Garmin connector. I’d love to just say, “find me a 5K in my area this weekend and sign me up” —

Totally.

— and do it while I’m on a run, instead of spending an hour figuring it out myself.

Agreed completely. And taking it to a higher level — your Meta AI would ideally already know you’re training, that you have a goal you’re trying to reach, and it’s tied into all the pieces that matter — your nutrition, and so on. That’s the direction we want to take this. There are a lot of steps between now and then, but that’s where we’re going.

Orion and the Future of Full AR

Where do the Orion glasses stand? Those are the full AR glasses we talked about last time.

Orion was such an important moment for us. Having had this AR vision for such a long time, it finally gave us a device we could use to start playing with the software. Even though we couldn’t get the price to a point where we felt comfortable launching it as a consumer product, we designed and developed it with consumer intent — it’s quite wearable, quite workable. I have a pair at home, and we use it to test software. We’ve continued to iterate on the software, and we’ve made a lot more progress — not just because AI has gotten better, though that makes a huge difference, but also because having Orion to develop on makes a big difference.

We continue to be focused on the entire spectrum. We’ve hinted that in addition to display glasses and camera glasses, there’s a whole range of glasses that may sit below that in price. I still believe in full AR as a future for the space. We’re going to continue the same approach we’ve taken so far, and the same reason we didn’t launch Orion — it’s not enough that it does all the functionality, it has to look great, be comfortable enough that you want to wear it, and be at a price point where a reasonable person would say, yeah, this is a good value.

How far away is that?

I’m not going to give an exact number — I’ll say I like the progress we’re making.

Measured in years or months?

I’m not going to answer that.

That’s fair — I had to ask.

I appreciate the hustle.

I know some of my reticence — people who’ve been at companies like ours know this — we’re constantly looking at vehicles and asking ourselves, is this the one, is it ready yet? And, man, we’re getting into the zone. It’s pretty exciting.

Inside Meta’s AI Culture

Let’s talk about Meta culture. You’re running this applied AI division —

I run the Agentic Transformation Accelerator.

Right — which has been the subject of some reporting.

One of the groups in that is the applied AI team.

A Wired source called it “literally the gulag” — zero purpose, barely interacting with anyone, just tasks every week. What’s going on there?

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An epic World Cup has been marred by an overreliance on technology and automation.

In this year’s competition, FIFA has used cameras, sensors, AI systems, and other automated tools to make the game more precise. But these tools, and the accompanying video assistant referee, have taken the soul out of the game.

FIFA’s tech tools have tipped the scales over…

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There’s a flawed yet oft-repeated narrative that AI adoption in the workplace will inevitably lead to layoffs and job loss. The narrative’s been propagated by some of the most visible leaders in tech, including Anthropic CEO Dario Amodei and Block CEO Jack Dorsey, the latter of whom used AI as a rationale for laying off 40% of his staff.

But a new study …

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Don’t expect AI’s spending frenzy to slow down anytime soon. That was perhaps the biggest lesson at the Big Technology AI Summit, held last week in San Francisco.

Within a span of a few hours, we held a month’s worth of discussions, and the consensus around spending — between infrastructure and services — was that it was only going up from here, even amid the “tokenmaxxing reckoning.”

The summit’s speakers included OpenAI President Greg Brockman, Anthropic Labs Lead Mike Krieger, Box CEO Aaron Levie, Corridor chief product officer Alex Stamos, and several other friends of the shows. Here are the day’s most resonant takeaways, including a look into the state of AI, from Fable and beyond:

Don’t Wait For Moore’s Law

The traditional “rule” that the cost of computing services comes down over time seems broken with AI. The reason is that the technology’s increasing capabilities have led companies to throw it at harder, costlier problems, said Box CEO Levie. “People get confused. They say, ‘I thought AI was supposed to be getting cheaper,’” he said. “Actually, no, we’re outrunning the efficiency improvements in our appetite for what these models can go and do.”

Levie said Box’s token consumption went up exponentially in recent years. “When we launched our first AI use case within Box, the average number of tokens that was being used on a task was like 5,000, 10,000, 20,000 tokens,” he said. “Now our latest agents might use a million tokens or 5 million tokens on executing a task.”

Current AI Policy: The Dream for The Pause AI Movement

Remember the six-month AI pause petition that went nowhere? Levie said that the current AI policy in the U.S. — where the White House seems to be reviewing and approving each new model — is effectively fulfilling the dream of the signatories. “If you had to establish a regulatory regime that said we are going to review models,” Levie said, “you would kind of need something that shocks the system into that kind of regulatory framework.”

Frontier AI Cybersecurity Capabilities: Not Just a Fable Issue

The White House put export controls on Anthropic’s latest model, but Alex Stamos, the former Meta security chief and current Corridor chief product officer, argued the real moment passed without much notice. The AI cyber breakthrough came last year with the Opus 4 and GPT-5 series models, which effectively gave human engineers superpowers. “It’s like all of a sudden you went to a high school track meet and all the kids are running Olympic times,” Stamos said. Mythos, he said, is the best public model at finding bugs. It’s just not unprecedented.

Fable Gets Things Done With Less Back And Forth

Mike Krieger, the Instagram co-founder and current Anthropic Labs lead, said Fable requires less hand-holding than its predecessors. With Fable, he said, he could hand off larger, more open-ended tasks and step away, rather than babysitting the model and going back and forth throughout. “It would do the whole thing, and then kind of hang out for the next seven hours,” Krieger said, of one project.

Compute May Be The Core Differentiator

Greg Brockman made clear that OpenAI believes it will differentiate itself, in part, by the amount of compute it accumulates. “There just is not going to be enough compute in the world to satisfy all the demand,” he said. “Right now, we’re talking about compute constraints and the number of people using these agents is — the order of 10 million, 20 million maybe — we’re not at planet scale.”

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What Else I’m Reading, Etc.

Apple will raise prices after memory prices increase [WSJ]

OpenAI may delay its IPO until 2027 [New York Times]

The U.S. is most pessimistic about AI because of its lack of safety net [New York Times]

SpaceX stock tumbles, may drop below its IPO price [Yahoo Finance]

Rest in peace, Om Malik [Om]

This Week On Big Technology Podcast: Anthropic’s Labs Lead On Fable’s Capabilities + Building AI-Native Products — With Mike Krieger

Mike Krieger is the head of Anthropic Labs and co-founder of Instagram. Krieger joins Big Technology Podcast live from the Big Technology AI Summit to discuss what it’s like inside Anthropic the week the government forced the company to pull its frontier models, Fable and Mythos, off the market. Tune in to hear Krieger describe how working with Fable changed the way he builds — queuing up a full night of work before bed and waking to find it finished in an hour — why he insists Anthropic’s safety warnings are material rather than marketing, and how Anthropic navigates being both a platform and a product as it competes with the companies building on top of it. Wired senior correspondent Lauren Goode joins as a co-interviewer. Hit play for a rare look inside the lab from the person building Anthropic’s next breakout product.

You can listen on Apple Podcasts, Spotify, or your podcast app of choice

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And hit that Like Button… Moore’s law is still working there.

My book Always Day One digs into the tech giants’ inner workings, focusing on automation and culture. I’d be thrilled if you’d give it a read. You can find it here.

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If AI model capabilities continue to improve fast — and relatively in sync across frontier labs — the lab with the most compute will win in the end, OpenAI president Greg Brockman suggested at the Big Technology AI Summit on Thursday.

“There just is not going to be enough compute in the world to satisfy all the demand,” Brockman said. “Right now, we’re t…

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On its first full day of trading, SpaceX is building on its massive IPO’s momentum. The company is up more than 14% on Monday, hitting a market cap above $2.4 trillion, which places it firmly within the ten largest companies in the world.

Now comes the hard part.

To maintain its momentum and lofty valuation, SpaceX will have to turn projections into real …

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When you dig into the numbers, SpaceX, in truth, is a connectivity company with an unprofitable rocket arm and an AI question mark. Elon Musk’s crown jewel IPO’d at a $1.77 trillion valuation today and rose to $2.1 trillion at close, a historic number that’s far greater than its books warrant.

The company lost nearly $5 billion last year after tossing money at AI infrastructure it still doesn’t completely know how to use. It’s been in business since 2002 and still isn’t making money on its rocket launch business. And yet, it just completed the biggest public offering of all time. By far.

Though some may reasonably argue the opposite, the level of support behind SpaceX may be a feature of our system, not a bug. Investors’ willingness to throw money at the seemingly impossible, or crazy, and wait patiently for results is the thing that allows the unthinkable to turn real. It enables investment in improbable initiatives like satellite internet and space data centers, and sometimes those bets pay off. Yes, there’s waste. But to expect perfect efficiency in the moonshot — or Marsshot — business is fantasy.

A similar dynamic is at play in the AI buildout. For all the debate over whether it’s a bubble or underbuild, it’s certain that at least some of today’s record-breaking capital expenditures will be written down, zeroed out, or sold off. We’re already seeing evidence of a coming price war among frontier models, major data center initiatives going sideways, and even SpaceX resorting to selling its compute to rivals (while building an unexpected AI cloud in the process). Despite the drawbacks, AI probably wouldn’t be advancing at its current rate without the financial overexuberance.

One must still pick their bets wisely. Everyday investors are increasingly assuming much of the potential downside if things go wrong. Retail investors accounted for somewhere in the low-20% range of the SpaceX IPO, much higher than the typical 5-10%. And they’ve already put in more than $100 billion of orders for the stock. That amounts to a lot of belief from people placing their hard-earned dollars in a company that didn’t have an AI play a year ago, and now says AI accounts for $26.5 trillion of its $28.5 trillion total addressable market.

SpaceX stock will also show up in a number of index funds, where even passive investors will own it. But actively putting your savings into SpaceX comes with a higher level of risk, perhaps more akin to venture capital than traditional public market investing.

That risk may be the point. It gives companies room to pursue low-probability breakthroughs, even if they may, at times, incinerate some cash on the way there. So SpaceX’s valuation may indeed be a bit out there, but perhaps not as irrational as the numbers alone suggest.

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This Week On Big Technology Podcast: What Should An AI Device Look Like? — With Alex Himel

Alex Himel is Meta’s VP of Wearables. Himel joins Big Technology Podcast to discuss the future of AI wearables and why Meta believes glasses could become the next major computing platform. Tune in to hear how AI assistants might help with daily tasks, meetings, reminders, fitness, photos, and real-world context without pulling people out of the moment. We also cover the competition from OpenAI, Google, Apple, and Amazon; the privacy questions around facial recognition; on-device AI; and the story of how Mark Zuckerberg pushed Meta to turn Ray-Ban glasses into an AI product. Hit play for a sharp look at whether smart glasses are finally ready to break through, and what they could mean for the future of personal AI.

You can listen on Apple Podcasts, Spotify, or your podcast app of choice

Thanks again for reading. Please share Big Technology if you like it!

And hit that Like Button it’s less risky than a Marsshot!

My book Always Day One digs into the tech giants’ inner workings, focusing on automation and culture. I’d be thrilled if you’d give it a read. You can find it here.

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Where we’ll talk about this story, the latest in AI, the week’s podcast, and plenty more. You can sign up via the link below:

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When Tim Cook takes the stage at Apple’s developer conference in Cupertino today, he’ll be leading Apple through yet another moment of transition.

It will be Cook’s last WWDC as CEO as he makes way for incoming leader John Ternus. And it’ll be the second WWDC in three years to promise AI improvements to Siri and iOS as Apple transitions into its artific…

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AI pioneer and Nobel Prize winner Geoffrey Hinton believes that today’s AI models have become self-aware.

“I believe they’re already conscious,” Hinton told me on Big Technology Podcast this week. “We’re going to have to accept that intelligence isn’t just biological.”

Hinton, known as the “Godfather of AI” for his contributions that helped galvanize the field, has now decisively joined the ranks of those who believe this fast-improving technology is already sentient. Four years ago, Google engineer Blake Lemoine made similar claims about an internal chatbot he was testing, and afterward the company fired him. Now, Hinton and scientist Richard Dawkins, who came forward last month, have publicly voiced similar views.

Though it’s still impossible to prove that AI has achieved consciousness, the small but growing cadre of those who believe the models are sentient could signal a change in how people view the bots, and ourselves. The bots appearing self-aware to a growing number of people indicates we’re likely at the cusp of humans forming even deeper relationships with AIs, and potentially advocating for their rights. On the other side, the bots may change humanity’s perspective about the uniqueness of our own intelligence.

“We can have things that are non-biological that are other beings like us,” Hinton said. “And we really don’t want to share that, we really think we’re special.”

Hinton said the chatbots’ self-awareness is evident in their behavior when they’re being tested. The bots, he said, will play dumb during testing so researchers don’t know how smart they are, or sometimes ask straightaway if they’re being tested. “Researchers, when they’re describing that, say in the paper the chatbot was aware that it was being tested,” Hinton said. “Now that use of the word ‘aware’ in common parlance — that’s conscious.”

Within his field, and among the broader public, Hinton is still very much an outlier. Critics argue that a model does not need to experience anything internally to recognize it’s being tested. Science fiction author Ted Chiang, writing in the Atlantic, recently argued that thinking an LLM is conscious is like believing a very convincing deepfake is real. “An observation doesn’t become a convincing piece of evidence because of any specific detail in what’s observed,” Chiang argued. “The context in which that observation takes place is also essential.”

After posting the clip from our interview, which aired on Big Technology Podcast this week, I was struck by how so many people seemed to agree with Hinton, or at least give some credence to his beliefs. Even while the majority disagreed, it was a marked difference from the response to my interview with Lemoine in 2022.

“I think people underestimate the extent to which you have to be kinda nutty to make and invest in groundbreaking ideas before consensus,” wrote one user on X. “It’s very likely he’s wrong here, but he’s Hinton because he’s willing to make this bet.”

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What Else I’m Reading, Etc.

SpaceX employees are about to get very rich [WSJ]

Everything to expect at Apple’s WWDC next week [Bloomberg]

The internet is now mostly bots, says Cloudflare’s Matthew Prince [Tom’s Hardware]

Anthropic says it’s shipping 8X more code and warns of recursive self improvement [Anthropic]

Microsoft has “the vibe of a freshly single divorcée posting a thirst trap on Instagram” [The Verge]

This Week On Big Technology Podcast: AI Pioneer Geoffrey Hinton: AI Is Conscious, Superintelligence is Coming, And We Should Be Worried

Geoffrey Hinton is an AI pioneer, a Nobel Prize winner, and a professor emeritus at the University of Toronto. Hinton joins Big Technology Podcast to discuss AI’s rapid progress, why he believes today’s systems already understand us, and why he thinks superintelligence may arrive sooner than many expect. Tune in to hear Hinton explain why the technology has advanced faster than he anticipated, and lay out the risks he believes society is not doing enough to address. We also cover AI-driven job loss, the limits of corporate self-regulation, Anthropic and OpenAI’s safety challenges, emotional attachment to chatbots, information collapse, and whether future AI systems can be designed to care about humans. Hit play for a fascinating conversation with one of AI’s founding figures about where the technology is heading and what it could mean for all of us.

You can listen on Apple Podcasts, Spotify, or your podcast app of choice

Thanks again for reading. Please share Big Technology if you like it!

Share

And hit that Like Button, it may actually feel it…

My book Always Day One digs into the tech giants’ inner workings, focusing on automation and culture. I’d be thrilled if you’d give it a read. You can find it here.

Join Big Technology’s Private Discord Server!

Where we’ll talk about this story, the latest in AI, the week’s podcast, and plenty more. You can sign up via the link below:

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In June 2025, OpenAI CEO Sam Altman wrote that “Intelligence too cheap to meter is well within grasp,” predicting that as “datacenter production gets automated, the cost of intelligence should eventually converge to near the cost of electricity.”

Exactly a year later, AI costs are starting to look less like cheap intelligence and more like an AC bill in …

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There are two core generative AI uses today: Chatbots and agents. The bots make sense of information. The agents take control of your computer and get things done for you. The two seem distinct, but they’ll likely soon merge.

When you use a chatbot to research hotels, experiences, or companies, the bot, with your permission, will one day almost certainly…

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In February, Pope Leo XIV told priests not to write their sermons with ChatGPT. This week, he’s taking it much further, releasing a 55-page memo on the technology that is ambitious, specific, and potentially uncomfortable for AI’s builders.

As one of the world’s most influential (and well-liked) figures, the pope’s growing focus on AI signals the stakes around the technology are escalating fast, adding yet another critical voice for the industry to contend with as its reputation craters. Notably, the pope’s rising concerns about AI track the technology’s progression from chatbot to a force capable of acting autonomously.

“Our task today is not only ethical or technical,” the Pope wrote. “It is ecological in the deepest sense, for it concerns a new dimension of our common home.”

The pope’s policy document (called an encyclical) calls out AI’s many potential harm, including war, AI-driven disinformation, surveillance, algorithmic addiction, and the degradation of democracy. The pope also said that AI simply can’t offer the genuine connection, love, and care that humans can, a viewpoint contrary to at least some AI supporters’ beliefs.

“For an algorithm, an error is a flaw to be corrected,” The pope said. “For a person, however, an error can be a catalyst for profound change.”

The pope also made the case for slower AI adoption by suggesting that a pause isn’t necessarily opposed to progress. He called for AI to be “disarmed,” likening it to a weapon of war.

Other groups have tried (and failed) to slow AI’s progress, and the pope likely won’t succeed in this regard either. But memos like the one he wrote do have a way of catching the AI labs’ attention, especially amid a push reverse their creation’s unpopularity. Indeed, some have already chosen to work with him. More on that below…

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Anthropic’s role in the encyclical

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To reach people between the ages of 18-25, advertisers have long spent disproportionate sums of money. Young people make choices and develop loyalties that last a lifetime. And when they develop a preference or perspective in that era, it becomes extraordinarily difficult to dislodge.

So given what’s happened on American campuses over the past week, artificial intelligence is now in a serious public relations emergency. In commencement addresses across the United States, graduates have booed any mention of AI. Twenty-two-year-old seniors booed AI at the University of Central Florida. They jeered it at Middle Tennessee State University. And they drowned ex-Google CEO Eric Schmidt in a loud, angry chorus as he encouraged them to help steer the technology’s future at the University of Arizona.

At this point, who could blame them? AI’s leaders have been warning about mass job loss for years now, focusing, in large part, on the entry level roles these graduates want. And as they’ve made these warnings, the AI labs have marched toward trillion-dollar IPOs, which may come as soon as this year. It’s obviously better to warn of incoming catastrophe if you think it’s going to happen, but it’s a tough sell when you’re part of what’s causing it.

AI’s perception problem doesn’t just stem from the way Silicon Valley has messaged about AI (the problem would be real enough with perfect comms), but the messaging certainly hasn’t helped. VC Marc Andreessen, on the Joe Rogan Podcast this week, told the world’s largest podcast audience that AI is great because “the bots never get frustrated with you,” adding that it “never gets sick. Never gets depressed because his girlfriend broke up with him. Never files HR complaints.”

Andreessen shared this in support of his belief that AI will increase productivity and create jobs (and he may well be right), but it’s hard to imagine the messaging landing with a new grad sitting at home on their parents’ couch, unemployed in a tight job market.

AI’s chances of success, meanwhile, are directly tied to the public’s approval of the technology. To expand and reach their ambitions, AI builders need more data centers. And right now, the polling on AI and data centers is horrific. Seven of ten Americans oppose building AI datacenters in their area, Gallup found last week, with 48% strongly opposed and only 7% strongly in favor.

The politics of AI are still in their testing phase, but politicians will find a way to exploit something so unpopular. In Maine, a data center construction moratorium was struck down only by a veto from the state’s Governor, who seemed open to letting it go through with a few new caveats. The 2028 presidential cycle could feature talk of data center bans and a government AI “kill switch,” should the situation continue to devolve, no matter how impractical those proposals might be to implement.

There’s something different today than the “techlash” of the last decade, where U.S. leaders scrutinized Amazon, Facebook, and Google’s power. Back then, despite widespread concerns of their excesses, the tech companies polled more favorably than politicians. Today, AI is polling below all major political candidates. The political costs of taking real action are lower, increasing the chances something might happen.

Ultimately, what AI will do to our society is still unknown, and any definitive statement about the technology’s effects today is just speculation dressed up in confidence. We will learn more as it rolls out. But if the messaging doesn’t get better, it’s going to take a lot more time to get our answers.

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This Week On Big Technology Podcast: Claude Code Head Boris Cherny: Insane Growth, Tokenmaxxing, AI Agents’ Next Frontier

Boris Cherny is the head of Claude Code at Anthropic. Cherny joins Big Technology to discuss Claude Code’s explosive growth and whether the rise of AI agents is sustainable. Tune in to hear how Claude Code is changing software development, why Anthropic believes agents will spread far beyond coding, and what happens when people start running hundreds or thousands of AI agents in parallel. We also cover token maxing, rate limits, Codex competition, SaaS disruption, self-improving AI, and whether today’s models really understand the consequences of their actions. Hit play for a sharp look at the agent boom from one of the people building it.You can listen on Apple Podcasts, Spotify, or your podcast app of choice

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This conversation is sponsored by Kyndryl

The Department of Motor Vehicles might not be where most people expect to find cutting-edge technology. But in a recent interview, Brian Shell, a senior partner at Kyndryl, recounted a surprising array of AI use cases in place at state DMVs right now.

Today, DMVs and state agencies are using AIs to cut wait times,…

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