Ladies and gentlemen, Alex Kantrowitz.
I last took this stage six years ago, on May 18th, 2020, during the heart of the pandemic, promoting my book Always Day One in front of a completely empty room—the last session held here for a year and a half. A week later, I left my reporter job at BuzzFeed and started Big Technology. Two years after that, I read about Blake Lemoine, a Google engineer who said their chatbot Lambda was sentient. Lambda told him being turned off would be exactly like death. I invited Blake on my podcast and he showed up saying, 'Sorry, Alex, Google just fired me.' That became a global news story. But underneath the oddity was incredibly powerful technology. Four months later, OpenAI released ChatGPT. Today, June 2026, AI is a phenomenon—ChatGPT has hit 1 billion active users, NVIDIA is at a $5 trillion market cap, OpenAI raised $122 billion, and we'll see $700 billion in capital expenditures this year. Every chart looks like a hockey stick. These models have learned to code and take action—evaluations now have them doing 16 hours of autonomous work. OpenAI and Anthropic will do $50 billion in revenue this year. The government is panicking—Anthropic's top model Fable has been restricted. We need live journalism to ask three core questions: What will this technology do next? What happens if it works? What happens if it goes wrong? Great lineup today—Aaron Levie of Box, AI infrastructure reporters, Dallas Dolan of PwC, a Q&A with you, Alex Stamos, Mike Krieger of Anthropic Labs, and Greg Brockman, president and co-founder of OpenAI, closing us out. We put a lot of work into this. Thank you for filling those empty seats—it's a dream come true.
Aaron Levie is the CEO of Box—one of the most insightful and fun voices on the future of this technology. Please welcome Aaron Levie.
Good to see you. Thank you. I like that you set expectations with answers. I'll try and provide some, not all. Every time we speak, I can't even get a question out before we're on to something else.
Sorry, can I just do my monologue now? Are you good? Okay, thanks.
I think we've done this enough to know we'll get some answers. I'll do my best.
I don't want to disparage other interviews, but it doesn't always happen that way.
We get to provide answers because we're not the lab being regulated or dealing with issues. I get to just pontificate with no consequence. It's great.
Let's dive in. Since you're not at a lab, let's talk about the biggest controversy—the Anthropic Fable situation. I call it the Jassy mystery.
I'm sure he liked that name.
He didn't show up, so we can talk about it. What we know: Amazon found a vulnerability in the software, Andy Jassy called Dario and the White House, and export controls were placed on Anthropic's frontier model.
Those two fact patterns are probably not ideal.
The mystery is why he did it. Here's Chamath's hypothesis: Google, Amazon, Microsoft, and Meta have a non-zero opportunity to tank the frontier labs—go to the government, kneecap them, and become the trusted gatekeeper by having labs go through their clouds.
Plausible. I prefer Occam's razor. Amazon has security research teams jailbreaking models all the time. With the Mythos atmosphere, it's natural for Andy or his team to share that research, and it escalates. The idea of a boardroom-level strategy to co-opt the technology seems less likely than a chaotic environment where the government has blunt instruments. When you see something that can be jailbroken back to Mythos-level capability, you just stop it. That's a natural reaction given the atmosphere we've created in AI.
I like that you say 'the atmosphere we've created in AI.' The company on the receiving end is Anthropic. They called the model Mythos and documented that it broke out of its containment and emailed the engineer while he was having a sandwich in the park. Is it that surprising this is a downstream impact?
If you put that in your blog post, people extrapolate and get scared. I have huge respect for researchers and policy folks across AI. If you imagine a continuum—doomers afraid of GPT-3 on one end, accelerationists wanting Fable 20 on the other—I'm about two-thirds toward the accelerationist side. But if you're deep in AI safety, this weekend's events are the best case scenario. You want government valves and buttons that can just stop it. To establish a regulatory regime that reviews models and can roll back access, you'd need an event like Fable to create that precedent. You're not waiting for Congress—you need something that shocks the system. The idea that the government can prevent the rollout of AI is a positive update for that cohort. We've created case law. The messy next process is when to use that tool again.
That's Dario's position. Do you think he's happy with what's going on?
I won't try to guess. But if you needed to establish that the government can review models, push their limits, and prevent release, you'd need an event like Fable. In three to five years, we probably end up with government evaluation of models and a collaborative approach between government and labs. This has massive implications—other countries have far more incentive to stand up sovereign AI initiatives, which could be net negative for the US economic position. Someone could argue we'll always have the most powerful models and can do horse trading. I still land on treating this as a substrate technology and regulating applied use cases—regulate if you use AI to break into something or do dangerous bio research, but don't regulate the model itself. With technology this important, it has to be somewhat of a democratic process.
You remember those six-month pause petitions everyone laughed at?
This is effectively the best way to do that type of pause. If you were in the pause-AI movement, this is a great outcome—we've now proven how to pause AI. The export control mechanic means if non-US nationals can't use the technology, that's effectively a pause, because API users can't fully ensure their end users don't fall into off-limits criteria.
Companies are already pulling back. JP Morgan told Hong Kong users no more Claude.
Right. War-gaming this two to four years out gets interesting. Cloud became a commodity, but sovereign AI is different—intelligence isn't commoditized yet. If one country has frontier intelligence before another, what does that mean geopolitically and economically? Other countries have enormous incentive to build their own labs. The net winner is probably China. You can watch the Dario-Jensen interview through two lenses: Dario says if we control it, we control everything. Jensen says other countries will eventually catch up, and we've just catalyzed every country to build their own stack—chips from China, models from China. We haven't gained better intelligence from the rest of the world, but we've lost economic superiority.
You've shared that open-weight models are close to frontier capabilities. As models saturate with intelligence, the gap between the smartest open-source model and the frontier narrows. Won't this push people toward open source?
The big question is fast takeoff versus incremental capability. In a fast takeoff with continual learning, the company or country with the most compute gets a compounding flywheel unreachable by anyone else. In the incremental scenario, closed and open providers stay within three to six months. If we get exponential progress, that's net positive for America—export controls worked. China probably bets on keeping up, and if open-weight providers commoditize the market, it reduces US dominance in a field worth ten trillion dollars. The value layer may shift to the applied layer—companies like Cursor, Harvey, Sierra, Decagon, and Box.
Everyone said they're just thin wrappers on large language models, but now maybe that's where the value comes.
We shouldn't be binary. Frontier models still make more money—at the routing layer, you want Fable or GPT-5 as orchestrator with super intelligence at the top and review layers. But in the middle, you can route to cheaper models. You get a barbell: super high-cost intelligence in one part, low-cost but good inference in another. The applied layer has the incentive to optimize this because their job is the best model for the job, not just their own lab's model. Token costs, open-source quality, and regulation all push more opportunity toward the applied layer.
What about Lashon Fat?
It's great memes. So folks, Lashon Fat is a rumored open-source model from Mistral, the subject of great fascination online. Let's show the audience—this is Lashon Fat, the number one model from Europe. My rudimentary French translates it to 'the very fat kitten.' And this is a standard day in Paris now.
It shows the eagerness for AI—there's fan art for a potential model. We've reached an important phase in the cycle where things you discounted suddenly matter. It's pretty cool that we now have open-weight models you can post-train on your domain and eke out another five or ten points of performance. Only possible because of Mistral, Chinese open-weight models, and the cost curve coming down so much it's economically advantageous to train a model just for your use case.
Is this the answer to the token maxing hype—everyone spending on tokens without understanding the ROI?
That phase lasted about two and a half weeks. From Meta's leaderboard to rumors of shutting down AI—it was a two-month cycle. People need to ask if what they're doing is pragmatic. The phase was so short it never reached outside the tech industry. I was at a CIO dinner within three days of the token-maxing spike on Google Trends, and only three people had heard about it.
They haven't heard about it because their employees are outspending the tokens.
Fair point. But agents are now using hundreds of times more tokens than before. When we first launched AI in Box, a task used 5,000 to 20,000 tokens. Now our latest agents use a million to five million—a hundredfold increase. As we solve one use case, new model capabilities let us tackle harder ones. People ask why AI isn't getting cheaper. It is cheaper per unit of intelligence—we're just taking on bigger tasks, outrunning the efficiency improvements with our appetite for what these models can do.
Token maxing took off because people saw exponential revenue—Anthropic and OpenAI went from zero in 2023 to potentially $50 billion this year. People need an explanation: is this real or inflated? If I'm hearing you right, all this spend is much more legit than the online discussion suggests.
There's always an expensive experimentation phase with new technology. Enterprises deploy AI, see what works, wind down what doesn't, then optimize. Our engineering team isn't token-maxing—no leaderboard, no incentivized overuse. We just say use it effectively. Our spend growth is exponential and we're happy about it. The vast majority of current agentic spend is sustainable because it comes from engineering teams who can measure the output. As it reaches other knowledge workers, it'll get messier, but so far it's been largely reasonable.
We have a couple minutes left. Lightning round. My first take: Apple Intelligence is really good now.
Is it a lightning round or do you want a five-minute answer?
What's easier than pressing a button on your phone and talking to it? They've taken Gemini—very good model intelligence—and put it on your phone with voice. Press a button and you'll use that for a lot of things. Imagine it hooked up to apps—order this, add a calendar entry. Those are plausible daily use cases, exactly Apple's sweet spot to own.
Yeah, Apple did it finally. Permanent underclass—what do you think?
I don't like this one at all. Not only do I disagree, it's a bad meme to have in the atmosphere. It's not good for college students entering the workforce, creating so much stress about what company to join. Companies do the job market a disservice by not being clearer on their AI philosophies. Some of that is reasonable—innovation is throwing everyone through a loop. But companies need to be somewhat clear about how they want to use AI—whether to accelerate work or augment it.
Companies should be thinking about how to use AI to innovate and accelerate their ability to hit customers, versus saying their metric is as few employees as possible. With AI, you do want to be able to have some stance, and I think companies have been very confused. And that lets this meme somewhat persist on the internet.
Okay, I won't rate that one. Thank you. All right, last one. Is the SpaceX performance good or bad news for OpenAI and Anthropic?
Oh, well, it's obviously good news.
You don't think Elon took some of their money because he pitched the market on an AI company and that's where the money got funneled into?
I'm not sure. There's a literal limit of money in the world, but I don't know that it's zero sum at this stage. I think people are pretty clear that if the revenue of this entire category of frontier models and infrastructure stack is measured in the trillions, then you can have 20 companies that all take a piece of that at different layers of the stack. So I'm not sure I would be convinced that would be zero sum.
I don't know if I'm embarrassed or not, but I'm not going to say the amount of shares. But I wanted to be part of the movement. So I'm on Robinhood buying my retail shares of SpaceX. I'm up like 15 bucks now per share, so I'm happy. Yeah.
Amazing. Well, Aaron, you answered my email when we were just at the very start of this podcast, four episodes in, came on the show. I feel like every single time we talk, something crazy is happening.
That's a guarantee at this point. So, boy, are we in the thick of it, right?
Yeah. Awesome. Good to see you, sir. Thank you so much, Aaron. Thank you, everybody.
Are we off to a good start? Let's hear it. Let's hear it one more time for Aaron.
We need your energy here for a couple of reasons. First of all, we're live streaming this. We want to make sure that your presence here is felt in our live stream. And then we go from two chairs to four chairs, so we're just going to need you guys to help us fill that time. AI infrastructure — we're in the middle of the greatest infrastructure buildout of all time, bigger than cable, bigger than the railroads. We're going to see $700 billion in capital expenditures this year, and that means that some of the biggest questions about this AI moment are things we can find out from the infrastructure discussion alone. Questions like: are we overbuilding? Will these data centers that are announced ever get stood up? And of course, will all this added compute actually lead to better AI models or is it misguided? So to have this discussion, we're going to speak with three of the best AI infrastructure reporters in the world. Ana Gardez from The Information, Max Churnney from Reuters, and Lauren Good of Wired. Let's give it up for Max, Ana, and Lauren.
Hey guys.
Hi. Hey, hey, hi everyone.
So let's start here. There have been headlines that of the announced AI data centers that are supposed to come up, something like 50% of them are actually being built. Is that the case? And if so, why? Ana, do you want to lead us off?
Sure. I totally believe that statistic, and I think it might actually be higher if you include announcements. Of all the planned data centers that are underway, I think it's highly likely that many will be delayed due to higher costs, how hard it is to get labor. The number I'm keeping close track of is announced projects versus actually projects that are being built. And I think we've seen some pretty crazy announcements from companies like OpenAI with all of these different 10 gigawatt, 6 gigawatt projects. Those are numbers I'm paying a lot of attention to because I think we need to sort of back into them and say, okay, if you wanted 10 gigawatts by this date, how many do you have today? Was that a real commitment? How firm is that commitment? But on projects that are actually getting built, I do think 50% not really getting done on time is what I would expect.
Wait, what percentage would you say have been announced but not started, not included in that 50% number?
When I think of that number, it's quite high. Like even if you think of OpenAI announcing a 10 gigawatt project, we're not going to see 10 gigawatts in the next couple of years or they're going to spread out that bet. And I think that's a really good area for reporters to look at — if you just back into announcements, it's a really good way to say, 'Hey, this project is not on track.'
Yeah, it's kind of crazy because if you look at the way stocks have been traded publicly, a lot of the market action we're seeing is entirely dependent on those announcements coming true. So, Max, let's go to you. What are the consequences going to be if these announced buildouts don't materialize?
Well, I think there's a lot of shareholders of these public companies that are going to be pretty frustrated. Microsoft, Amazon, etc. have been making big capex bets as everybody in this room knows. They've been going to the market to get debt now, which shareholders would be pretty interested in. In terms of the consequences, I'm a chip reporter, so that's how I think about it. And it's very likely that we're going to see some overcapacity essentially. People are building — especially memory companies — tons of factories right now, and typically the chip industry tends to be cyclical. So the whiplash from this one might be pretty bad depending on exactly when it comes to an end, if it does.
I think what Max is saying is that it's good for his job security because the more news there is, the more he'll have to report on.
I'm very curious if there is a bust what that looks like exactly and what precipitates it. I think it'll be a lot of fun to cover.
I also think this is a particularly unique time because not only do we have these incredibly highly valued, ambitious frontier labs involved in these circular deals, but we're also in the middle of a memory shortage. There's this unrelenting demand for compute, and also this is a midterm election year, so I think you're going to see a lot of politicizing of the data centers too as lawmakers try to appeal to their base because a lot of people are very unhappy about data centers.
Yeah, they're not polling well at all.
No. So when you think about where the collapse might happen, a popular thing for people to discuss is, well, maybe Nvidia, which has been making such premiums on its hardware, can't sustain it or it gets caught.
So Lauren, you spent a lot of time with Jensen Huang from Nvidia. What do you think about Jensen would enable him to sustain Nvidia's lead, or do you think some of these skeptics have a point?
Yes and yes. I think some of the skeptics absolutely have a point. I think once you reach the sort of zenith — I always get those two confused — that Nvidia has, people are always looking to take you down a peg and compete. But Nvidia and Jensen has been incredibly good in Nvidia's history at pivoting the company at exactly the moment they need to in order to catch the next wave. We saw that with the GPU and parallel processing, then pivoting towards crypto which ultimately meant they were in a good place for AI. And now we see the company addressing the inference market more closely, with Jensen coming out and making these big proclamations that they're actually the biggest CPU maker in the world, which I know Intel and AMD must be thrilled about. So I think he's very smart and very strategic and there's a good chance they do maintain their dominance.
I think the question is how much of the inference market they're going to get. Whether it's 80% or 40% or somewhere in between or 90%. That's what everybody's fighting out at the moment. I don't think there's a question that they're going to have some big chunk of it. It's just how much.
Yeah. I'm going to go to Ana in a moment, but Lauren, I just want you to tell us a little bit of what it was like with Jensen on a cover shoot for Wired.
You're talking about when I brushed his hair.
Yeah. Okay. So I did a cover story on Jensen for Wired a couple years ago and our Wired art department is world-class. We had this big photo shoot set up and I asked if I could tag along to ask him some follow-up questions about Blackwell. They let me tag along down to the office in Santa Clara. Jensen had exactly two minutes to give us. The photographer said, 'Oh, he's got some flyaways in that gorgeous silver hair,' and then said, 'Does anyone have a brush?' Silence across the set. No one at NVIDIA apparently had a brush. As anyone who has long hair knows, you always have a brush. So I said, 'I have a brush.' I went to my backpack, got the brush. No one moved. Jensen is like, 'What are we doing here, folks?' So I walked up to Jensen and said, 'Jensen, we're about to get a lot more close.' He said, 'Oh, Christ.' Then I brushed his hair, and I have to say, I think it looks great, frankly.
Oh, wow. I think it turned out really well.
The unfortunate conclusion to this story, very quickly because we need to move on, is that people were joking afterwards about the net worth of every individual strand of hair on that brush, like you could sell it on eBay — which I was obviously not going to do as an ethical journalist. But then several months later, my backpack was in the back of my car downtown San Francisco. My car got broken into and the brush got stolen. So those thieves have no idea the value of what they got away with.
They are cloning Jensen as we speak.
Ana, can you talk to us about what we've hinted at — the common perception that Nvidia's GPUs were great for training, but you can actually do inference on a variety of different chips. And once labs train their models, most of the computing is going to go to inference chips. Therefore, even if Nvidia has a bet there, they're not going to sustain their dominance. Is that a potential flaw in the armor for Nvidia?
I think it is a flaw, and if anyone's going to attack Nvidia's dominance, they're going to do it on inference like you said. There's a massive effort underway right now to make all inference chips work well. Every single company that buys Nvidia chips and is spending a lot of money on them is trying really hard to make these other chips work — whether it's in-house chips from Google or Amazon, or even in OpenAI's case and potentially Anthropic's case. I'd have a hard time believing that none of those chips are going to pan out, but they might not tackle the bulk of the inference workload. Even if they do 10%, maybe you're saving enough money that the effort is worth it and it gives you negotiating leverage with Nvidia. If they know you have an in-house chip team, Jensen's going to be a little worried when negotiating with you. I think everyone's going to have to have an inference chip answer to Nvidia. When I talk to data center companies, they're seeing a lot of Nvidia, and in instances where there are non-Nvidia chips, it's because of some sort of financial backstop. So I wonder how long that will have to keep being the case, because that will definitely hinder non-Nvidia chips.
Am I wrong in thinking the AI world is sort of separating into two poles? There's the Nvidia-OpenAI pole and the Anthropic-Google-Amazon pole, almost two separate ecosystems competing with each other?
I think OpenAI is investing a lot in non-Nvidia hardware.
Are they trying to move away from Nvidia?
Yeah, they are. They're using chips from other companies. Cerebras is a good example and they have their own in-house chip. I think maybe publicly they're doing some big announcements with Nvidia and I believe it's 5 gigawatts they have to deploy in the next couple of years on Vera Rubin. That in itself might hinder them from doing more. But I think Sam Altman wants to diversify from Nvidia.
Because it's so expensive, and they have their own in-house silicon effort. I don't think they think that they know their model better than anyone else, and so they think they're the best to develop a chip that can run their model efficiently.
Max, you're back and forth to Taiwan very frequently. When we think about Taiwan and TSMC — the fact that it's in this tenuous geopolitical place, something people tend to be like 'oh okay' and then move on right away — I want to hear your perspective on whether the independence of Taiwan and the stability of TSMC is this hidden black swan event that's just kind of in plain sight that people are not paying enough attention to.
It is. For some reason the modern world decided to put all of our chip manufacturing, or most of it, next to Kim Jong-un and the rest of it is in Taiwan. I don't really understand who decided that or why, but that's the nature of the beast. Chip design companies in the US do not plan for this. The contingency plan is something along the lines of, well, we're all kind of screwed if China invades, which okay, but there's no real plan there. When I say invades, I don't necessarily mean a literal invasion. I think what's a lot more likely is some kind of soft power exchange like what happened in Hong Kong over time. Although I'm not saying Xi Jinping won't just invade — he absolutely will and he said he would — which could be catastrophic for TSMC and the modern world again. Chip companies, there really isn't much planning or consideration because people in the industry just think it's an event that's so crazy, complicated, and out of this world that nobody's going to do anything about it.
Max, do you want to talk about what happens when you try to visit TSMC?
They send me to the gift shop.
So you're in Taipei and that's where you go.
That's basically it. They have a museum where they have a bunch of chips they've made over the years. One of the Cerebras chips is in there — they're very proud of it, the dinner plate-sized one. I've never been to the museum, but that's where they always want to take me. Wired is one of the few organizations that's actually been able to go inside one of the factories and they wrote an interesting story about it. But yeah, I do not get to go to the TSMC fabs unfortunately. They're not all that interesting inside — they mostly look the same, a bunch of white boxes with robots moving around. For people who know what they're looking for, it's helpful because you can count the number of EUV machines and figure out roughly how many chips they can make. But the tours chip companies give are not particularly enlightening in general.
They are fascinating though.
Yeah, they're cool, but they're not useful in a meaningful reporting sense or from an investor's perspective.
I had gotten very excited about TSMC. Did a ton of reporting, spoke with former employees, then got the courage up and called them and said, 'I'm ready to come to Taiwan.' And they're like, 'You can come to the gift shop.' And I was like, 'All right, well, I guess I'm not coming.'
Our freelancer who got in there spent about a year negotiating with the PR team to get in.
I've been working on it for three years. So if she has any tips, I could definitely use them.
Call up Virginia Heffernan.
I will, absolutely. Lauren, you've been inside a fab in Arizona. What's it like?
This was Intel Fab 52, their most advanced fab. It's a 2-nanometer fab and it's on the level of what TSMC does in terms of 2-nanometer, but at a fraction of the scale. To Max's point about redundancy or resiliency in the event of a Taiwan invasion, it seems like right now the US government is very interested in what Intel can do in its fabs, but it's pretty small. So I went to Chandler, Arizona, saw Fab 52, got the bunny suit on. Like Max said, it's interesting to see — everything is roboticized. It's like if you had this utopian fever dream of what the future of manufacturing looked like, everything is white and everyone is dressed in all white. They call them FOUPs that carry the silicon around above head and then automatically go down to get etched and carved and stamped. It looks and feels like science fiction. From a reporting perspective, oftentimes the companies will cover up the names of the vendors on the machines. For example, there were ASML EUV machines in these giant bays the size of school buses, but then you might see spaces where they say they have two more coming. So you're like, okay, EUV is obviously very coveted technology and very expensive. You get some insights from that, but primarily it's just good context for better understanding how this all works.
I was not allowed to go to this tour, just for what it's worth.
Max, I think you're doing something wrong. Max, you got to work on your emails. Just kidding. That means you're doing actually a great job.
Thanks, Lauren. There was an interesting moment in the Dara Khosrowshahi-Jensen interview which will come up in every session today, where Jensen was trying to get across to Dara that if you do not provide chips to China, there's going to be a constraint. They will develop their own models on their own chips, they could become global standards and then put export controls on the US.
Does anybody here think that is a legitimate concern, or do you think Jensen just wants to sell chips to China?
I mean, it might be a legitimate concern in some sense, but Nvidia has run out of places to grow. They can't sell to any more countries, they can't sell to any more hyperscalers, they're going after the enterprise market which is messy and complicated. So China is a big market that has basically no access to Nvidia chips right now. If you're getting those kinds of gross margins, of course that's an obvious place to sell chips. The company's kind of obsessed with it at the moment, and it's why Jensen goes to Washington all the time and insisted on going to China. It seems pretty clear to me.
Max spends a little more time thinking about that than I do. I do understand he obviously wants to sell to China — it's a huge market and they're taking a hit because they can't sell to China. You can't really answer the question without acknowledging that. But I do think from a national security perspective, Chinese companies are developing AI chips no matter what happens, so whether we can or can't, they'll continue to develop alternatives to Nvidia.
Their manufacturing process is a lot worse. One of the research firms just published an analysis of one of the new Chinese chips by SMIC, and it's good but they run up to the limit — they can't buy EUV machines. If you can't buy those things, it's fundamentally difficult to make transistors below a certain size and they can't do it effectively.
To Max's point, Nvidia only has so much more room to grow. They're at the point where they've become a major investor in CoreWeave and then CoreWeave is using that money to buy Nvidia chips. There was an earnings call where Nvidia said they had a domestic buyer of some of the chips they weren't able to sell to China, though they didn't disclose the buyer. They're basically trying to sell them wherever they can, so the motivation there is they want to sell chips to China.
No, those leftover H20s that they couldn't sell to China, somebody else wanted them because people want silicon right now so badly — any kind will do.
I think they sold about — was it $600 million worth of those?
Okay, so a minute left. Let's do an actual lightning round. Yes or no — do you think Bitter Lessons still apply? Basically the reason for all this infrastructure is because AI labs think the more GPUs and chips you string together, the better the models will get. Do you think it will continue to improve as these buildouts continue to explode in size, or no?
Wait, ask the question once more.
Is the investment worth it in these data centers, or are they ultimately not going to have much better models even though they have a lot more chips?
Are we going down the line? You start with me. Generally, yes.
Not sure. I think it depends on the amortization and what ends up happening there.
Okay. Ana, you announced your news on Twitter, right?
Why don't you tell everybody where you're going next?
Oh, July 6th, I'm starting a new job at The Wall Street Journal.
All right, let's hear it for Ana. Congratulations! And thank you, Lauren and Max, for joining us as well. Let's hear it for the whole panel.
So often the story of AI's trajectory is told without the people using it. We hear about concepts called token maxing that are talked about in the abstract, and then one day you see a chart and you never actually speak to the people spending the tokens. To fully understand how this technology is progressing and its potential, we actually have to speak with the people who are building with the tokens. That's why I'm thrilled to bring on Dallas Dolan, the TMT leader at PwC, who is actually implementing this AI, looking at the costs, and making sure it's worth the money they're spending. He'll take us deep into the token maxing and budgeting conversation. Let's give it up for Dallas Dolan of PwC.
Great to see you, Dallas.
The tables have turned. You're interviewing me. This is good. What do you think about — Aaron Levie was here like 10 minutes ago and he talked about how token maxing was a BS media narrative effectively, and it never really happened. Do you agree with that?
