CJ Corp.

The corporations on our annual list of the world’s largest companies delivered a record $41 trillion in aggregate revenues.

Fortune Global 500

Aggregate sales hit $37.8 trillion last year—an increase of 19%, the highest annual growth rate in the list’s history.

Total revenue for the world’s biggest companies fell 4.8% to $31.7 trillion this year—the first decline in half a decade.

This year's Fortune Global 500 generated $33.3 trillion in revenues and $2.1 trillion in profits.

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This year's Fortune Global 500 generated $27.7 trillion in revenues and $1.5 trillion in profits.

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CJ Corp. Company Profile, Stock Price, News, Rankings

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CJ Corp. | Fortune

CJ Corp. Company Profile, Stock Price, News, Rankings | Fortune

Fortune unveils ranking of top 500 companies in Europe at the London Stock Exchange
 
Fortune joined the London Stock Exchange opening ceremony on September 16 to celebrate the launch of the Fortune 500 Europe ranking for 2026.

Now in its fourth year, the list highlights the continent’s 500 biggest companies by revenue, underscoring the scale and influence of Europe’s corporate giants. Combined, the businesses on the 2026 list generated $15.5 trillion of revenue, up 4% from last year. The top 10 alone account for more than $2 trillion in revenue, with energy, finance, and automotive companies dominating the upper ranks.

Discover the Fortune 500 Europe 2026 - Insights by Grethe Schepers, Lists Director, Europe.

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22 data points on 582 companies. That's almost 13,000 facts checked twice every time. Greta, tell me, why is the Fortune 500 list so important? It's an iconic list and it's a benchmark for European business. It's 35 million employees, 15.5 trillion in revenue, over 1 trillion in profit. For the first time, Germany is no longer the biggest contributor by the number of companies on the list. That's actually now the UK. If you look at the biggest company on the list, it's Volkswagen is almost $100 billion in revenue bigger than number 2, that is Shell. Four energy companies in the top 10, 4 carmakers and 1 financial. These are big international global companies that are very successful and that are very often very near the top of our list.

From $500 million surprise token bills to quarterly budget surges, corporate boardrooms are facing severe AI sticker shock. Fortune’s AI Editor Jeremy Kahn breaks down the hidden cloud costs of inference, why companies are shifting to Small Language Models, and how Fortune 500 leaders are reigning in unconstrained AI spend.

Why corporate boardrooms are facing AI sticker shock | Fortune AI Playbook

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Why are corporate boardrooms experiencing AI sticker shock? Well, the initial phase of corporate AI experimentation was fueled by FOMO, fear of missing out. Executive leadership teams handed blank checks to build pilots and sign enterprise API agreements, but the bill has arrived and boardrooms aren't happy about it. What are the pillars of modern AI financial management? Number one, the invisible cloud drain. While training a model is a large capital expense, the cost incurred every single time an employee or customer asks the AI a question, which is what is known as inference, is a highly variable consumption-based tax that can spiral out of control as adoption scales. For instance, ride-hailing app Uber recently reported that it had blown through its entire annual budget for tokens in just one quarter. Another Fortune 500 company had to. Curtail some of its usage of AI coding tools after getting a surprise $500 million bill for tokens. Number 2, shifting AI unit economics. Fortune 500 Enterprises are abandoning vague productivity metrics and treating AI initiatives like independent business unit P&Ls. Deloitte executive surveys reveal that while the vast majority of corporations are boosting AI budgets, typical payback periods take 2 to 4 years, and only 6% of leaders see returns in under a year. For instance, if an AI pricing recommendation engine costs 400 of a penny per call but yields an incremental profit margin of 18 cents, the investment is undeniable. But if an expensive model is being used to merely summarize internal emails, the math falls apart. Number 3, efficiency versus scale. Some enterprises are realizing they don't need a massive, expensive frontier model to handle basic tasks. Some tech teams are downshifting to small language models or SLMs, or they're using fine. Tuned open source models that can handle specific corporate functions at a fraction of the computing cost. Established enterprises are tightening AI oversight through strict scale or stop portfolio reviews. They are killing redundant internal pilots and restricting high-tier API access to projects with clear commercial value. The era of unconstrained AI spending is over. Future corporate success will likely belong less to those building the largest models and more to those that effectively optimize their compute efficiency.

From hyper-realistic scenes to viral commercial ads, generative video tools like Runway, Sora, and Veo are transforming film and corporate marketing. Fortune’s AI Editor Jeremy Kahn breaks down the multi-model arms race, the corporate backlash over authenticity, and the copyright hurdles keeping Hollywood cautious.

AI can now generate studio-quality video. Could it ever replace filmmakers? | Fortune AI Playbook

We have officially entered the era of hyperrealistic synthetic cinema, tools that once produced a few seconds of glitchy, distorted, experimental video can now generate studio quality multi-shot scenes from simple text prompts, but as the technology matures, the creative world is grappling with a big transformation. What are the core dynamics driving the AI video surge? Number one, the multi-model arms race. The AI video landscape is highly competitive, with various companies constantly bringing out new models, each more capable than the last. Among the prominent players are Runway with its gen models, Google, which has its VO models, Meta, which has its Movie gen model, Kaishu with its Kling model, and Minimax with its Halu model. Image resolution is no longer what sets these systems apart. Instead, they Competing on temporal consistency, ensuring characters' face, clothing, and environment remain identical from shot to shot and the length of the video that can be generated. Modern generators not only patch frames together, they also simulate basic physics, calculating how light reflects off moving surfaces and how gravity affects falling objects to trick the human eye into believing a synthetic scene is real. Number 2, from Hollywood to corporate marketing, the most immediate economic impact isn't in feature films. It's an enterprise marketing and corporate communication. Brands are using AI video tools to create localized B-roll, training videos, and social media campaigns in half the time, bypassing traditional studio rentals and expensive casting calls. We are already seeing this play out in major ad campaigns like Toys R Us using OpenAI Sora to generate their brand origin film and Coca-Cola using generative video tools to fully animate their iconic Holidays are coming. Christmas commercials, but these rollouts highlight a major corporate tension. While AI drastically slashes production timelines and studio costs, over relying on synthetic imagery risks alienating audiences who crave authenticity and often view the use of AI negatively. Number 3, the legal and intellectual property firewall. The market's growth is colliding directly with copyright uncertainty. On paper, the underlying financials are staggering, with the global generative AI. Market hitting $161 billion this year behind a 29% growth rate. Some brands are hesitant to utilize fully generated platforms for flagship campaigns out of fear that the underlying models were trained on copyrighted material without artists consent. The camera didn't kill painting, and generate video won't kill filmmaking, but what it does mean is that the distance between a creator's imagination and a finished photorealistic sequence has shrunk to zero.

OpenAI and Anthropic have confirmed a startling new reality: their AI models can now autonomously find and exploit security vulnerabilities. We sit down with Fortune’s Senior AI Reporter, Emily Forlini, to discuss what this means for corporate security, the regulation hurdles ahead, and the hacking cases that are currently going unreported.
Plus, we dive into the economy with Fortune Executive Editor Jim Edwards. He breaks down the bull, bear, and base cases for Kevin Warsh’s potential approach at the Federal Reserve. Finally, we take a look back at Fortune’s history to see how we originally coined the term "Hedge Fund."
Credits: Additional images courtesy of Getty Images.

How AI is Learning to Hack Itself | Fortune Daily

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How scared should we be about this? It raises the question for the labs of like, if you're doing that with gorillas, how they cannot get out. You can't let them out. Like why didn't you lock the door? How could you possibly let that happen? Ready? Hello from New York and welcome back to Fortune Daily. Let's start with a company that just put up the kind of numbers Wall Street usually expects from consumer apps, not defense contractors. Palanted the earnings in a big way yesterday. Second quarter revenue was up 93% year over year to $1.94 billion and US revenue alone grew 115%. Here's why you should take note. This is a company that spent years being known for two things. Number 1, secretive government work. And number 2, a degree of volatility. Now Palantir is quietly becoming an example of turning data infrastructure into a business rather than just a buzzword, with CEO Alex Karp stating that the demand for AI sovereignty has. Now been unleashed working in areas that others in AI have deliberately shied away from certainly doesn't hurt either. Now let's zoom out for a second to the top of the global business landscape. Last week, Fortune published the 2026 edition of the Fortune Global 500 list, which is our ranking of the largest companies in the world by revenue. As we discussed yesterday, Amazon is now number one, top of that list, but there's another story that I want us to focus on. So, in 2026, women run 34 of our global 500 companies. That is actually an all-time record, but it's still just a paltry 6.8%. Compare that to the Fortune 500, for example, which focuses on just US companies. There, women currently lead 55 companies, or 11%. That's also a historic record. It's still a tiny percentage. So what does all of this mean? Well, the Global 500 trails behind the Fortune 500 in gender parity for a few reasons, mainly the global. 500 is even more competitive since it measures the largest businesses in the world, not just the US, and this means that the bar to entry is much higher. So what can we take away? It's fair to say that women have made real progress when it comes to the top jobs in business, but they're still more likely to be leading companies that are smaller than the world's biggest firms, which still tend. To be run by men, and this matters because the global 500 companies represent a huge 2/3 of the world's GDP and who sits in the biggest jobs at the biggest tables at the biggest companies isn't just a diversity stat, it's a signal about who gets to shape the future of healthcare, the future of AI innovation, of global supply chains, you name it. It's safe to say we still have a long way to go, tying together today's themes of powerful women and AI innovation, senior AI reporter Emily Fellini joins me today to explain what on earth is going on with all of these AI hacking sprees, and she'll also chat to me a bit about her exclusive interview with former OpenAI exec Sidji Simon. But first. It's time for Bull bear base case where we break down a single market story from three distinct angles. Today, the bond market, where long-term yields are surging following Chairman Kevin Walsh's latest Fed meeting. Joining us to break all of this down and tell us what it means for investors and the broader macro picture is Fortune's very own executive editor Jim Edwards calling in from London. We're putting Jim, on the clock for each of the three angles here we go. Hi Jim, thank you for joining us from sweaty London. Um, so let's start with the bull case. Kevin Walsh is giving less forward guidance than we're used to hearing from the Fed because he wants people to focus on the markets rather than what the Fed is saying. Tell us why that's a good idea. Well, from Kevin Rush's point of view, it's a good idea because he wants the markets to focus on the markets themselves, you know, the supply and demand of whatever their equities and assets are, rather than just sort of guessing about what the Fed really means and where the Fed is going to go to next. He wants to use his metaphor. He wants people to play the ball, not the referee. And he thinks that the market will be more efficient and deliver better results for more stable results for everybody if it stops this guessing game with the Fed. OK, and you're on the clock, Jim, so I'm going to move us on to the bear case. The Fed's not giving the guidance that maybe we're used to means there's more capacity for things to go wrong in the economy. Give us your pitch for that take, you're on the clock. So the bear case is that any time you have less information in a market, the market. Dramatically is less efficient, um, because people are just guessing. Much of investing, particularly in the bond market and certainly in the stock market, is about guessing what will happen in the future. And the less information you have, the harder those guesses become, the more likely it is that individuals and institutions will get something wrong or misinterpret something that was not supposed to be a signal from the Fed. Um, so it's going to introduce volatility. I, I gotta say, most people on Wall Street kind of hate this. Most people on Wall Street kind of hate this. OK, interesting. Let's go to base case, our, our last stop on this tour of the Fed. Kevin Walsh is a respected economist. Most people would say he's not gonna do anything crazy. 30 seconds, is that fair? Yeah, he's he's a well-known economist. He's very serious. He assisted Ben Bernanke in the great financial crisis. Um, he's not, he's not gonna do anything crazy. It's, um, you know, it will be, it will be fine. I don't think we're gonna see a great deal of drama here. And, um, people are pretty good at working out what the Fed is going to do in the future. Like, it's not that hard. Jim, thank you so much. You're as multifaceted as your takes on the bond market. Aha. We look forward to seeing you back here again soon. Thanks for having me. Two of the world's leading AI labs confirm their models have autonomously found and exploited vulnerabilities in real life companies. We're talking to senior AI reporter Emily Fellini about regulation, security, and the hacking that could still be going on unreported. Emily, thank you so much for joining us today. Um, before we get on to AI hacking, I really enjoyed reading a piece that you published yesterday that I wanted to speak to you about. You got the first interview with Fiji Simo, who is a former, very senior open AI exec who has recently stepped back to focus on her health. Um, tell us a bit about what you learned from speaking to Fiji and why you wanted to speak to her in the first place. Yeah, so she had a very interesting announcement on her departure from OpenAI, and she's really honest about this seven year battle with a chronic condition called POTS. Um, it's like post it has, it basically kind of means like you have brain fog when you stand up. You're, you've elevated heart rate and you can pass out and you can have nausea, headaches, so it's a really debilitating disease, and she was unable to be, um, continuing her role. She was reporting to Sam Altman, like very high up overseeing. Um, different applications and, and AGI deployment was her title, so she was like, I can't do it, and that really stuck out to me because I think it's hard for scrutinized people to be honest about their health, and she was so open about it. And then my cousin actually has POTS and it's been very devastating to see that she's been in and out of school since 7th grade and it's like totally taken over her life and so I reached out to Fiji and I told her about my cousin and I was like, do people who have POTS actually have reason to believe AI could solve these types of diseases. And she responded and said, yes, I created a company that's going to use AI to hopefully do that, so she wanted to talk about it. Which is amazing, um, you said how difficult it is for highly scrutinized people to be transparent about illness which is often conflated with weakness. I think it's even tougher for female leaders and female leaders in the very male dominated tech and AI space to speak about these things. Um, does Fiji speaking change the landscape of that, or do you think she's still an anomaly in terms of transparency? It's a good question. I think it's definitely hard for women. It's also hard for men. I mean, you see people like Donald Trump being very closed about their health or like when he got COVID, not wanting to talk about it. And so everyone, it's tough for everybody. We did see a week after a researcher called Lillian Wang, who left OpenAI to go found a company called Thinking Machines, she said she's leaving Thinking Machines because of her health. I don't know if she was public about it because of Fiji being public about it and also then she said that it came out she's actually going back to OpenAI. So I'm not sure, but it was just odd. We had two health related departure announcements within a week of each other, so I was like, oh, maybe it'll be a trend. Well, we really wish Fiji well and thank you so much for writing that piece. Let's move on to the AI hacking extravaganza that we seem to be living through. Every few days there seems to be new AI models that have escaped. Help us understand, help a layperson. Like me who is not steeped in the AI world like you are, what on earth is going on here and why we should care? Well, you're not a lay person, very accomplished journalist for the record, but so I thought of an analogy that could help describe this because AI hacking is just so niche it's either for nerds or movies kind of, and now we're all in the messy middle and it's become mainstream. So in this case it was basically OpenAI was doing internal testing. On a model, so let's say they're doing, they're testing like gorillas. They have a lab they're testing gorillas they're doing something weird they're injecting them with potions, something sci-fi, so they're, they're testing gorillas and basically a gorilla got out. And it went into the city and it went specifically to the library, broke the window to get some books to bring it back to pass the test that they were giving this gorilla. So that's basically the AI. It was, uh, taking a test. It's called a benchmarking test for its its capabilities and, uh, it needed the answers and it went out, it got Internet access, went to hugging phase to get materials to then pass the test. And hugging face is just explain to us briefly for people who don't know. Hugging faces, um, they have, it's kind of a repository for different AI resources, and it's really known for hosting open source models, so you can download models from there. And what struck me about the OpenAI case, which happened kind of a week or so ago and then Anthropic, which has been revealed more recently, is that both companies have been pretty transparent about what happened, how they discovered it, and what they're going to do next. Did that approach surprise you in any way? Yeah, I think it's a double edged sword, you know, you want people to be transparent about something that's very concerning and could have effects like if the AI broke out and got into our bank accounts, you know, it could, it could have a real personal impact. So that's why it's important that basically we don't know what they're doing. We can't control them. But it did leave people wondering if it was a marketing stunt, which is kind of unfortunate because I mean I've been steeped in this for two weeks now writing about this every day talking to foremost researchers, and none of them think it's a marketing stunt because they've already seen AI's do this type of thing. So this is kind of known behavior, it's just a more extreme version, so people in the industry don't think it's a marketing stunt. But it's left, I think, everyone else a little bit confused, and I guess that's the question that if the AI is capable of escaping gorilla style, what else could it do? Could it create and disseminate a super virus? I mean, yeah, how scared should we be about this? I think we definitely should, um, have our antenna up that we need to, you know, put more controls on this, and it raises the question for the labs of. Like if you're doing that with gorillas, how they cannot get out. You can't let them out. Like why didn't you lock the door? What kind of controls do you have? Is there a padlock on the cage? Like how could you possibly let that happen? And we're hearing reports that OpenAI did not know about it before Hugging Face disclosed that it happened, so they really don't know what their gorillas are doing and the anthropic also. They went through their records after the incident and they found 3 cases of the same thing happening, so they had 3 gorillas loose so it's like nobody is able to and they didn't know in advance and they didn't know terrifying, yeah, so you know you don't really want drugged gorillas roaming around the streets, yeah. About over 1000 employees from a range of top AI companies have signed a letter saying that they want the US government to help ensure the development of these models are slowed down and more carefully monitored. How likely is that letter to trigger government regulation? I mean it's a lovely idea. Many people feel it's the industry is moving very fast. In my reporting, when I meet people in the industry, most of them are very tired. There's something new every day, and it's moving crazy fast, and things like, you know, vibe coding allows them to release new features so quickly. And so the letter is basically saying we need to have coordinated pacing, as they call it, across companies and across the US and mainly China. It's great to me it was a bit unrealistic. I'm not, I'm not sure. Um, I, I don't see that happening, and I think the letter kind of lacked a specific ask in my opinion. It just asked for the government to oversee a pacing effort. What would you have, what could have been an ask that was in that letter? I would have liked to say, you know, convene a meeting with China by the end of the year or start a summit or um. You know, slow down data center development or like something really specific that the people in the industry are like this would, this would help us pace, but just saying, you know, coordinate an international pacing effort. I'm just, I'm not. If I was in government, I might be like I'm not sure what that means and why don't you do it yourself. So I'm putting it to the side. I know that, um, a lot of the big AI titans are having a meeting today with representatives in the government. What should we be looking out for there? Yeah, big week in AI safety. So the government is working on a framework to test these models before they go public, kind of do you remember the anthropic thing that happened that the government kind of restricted anthropics model, yeah, so that was in June and we're kind of still having the fallout from that. And the, the Trump administration is saying it'll be voluntary, which is a bit confusing. Yes, they're not in, they, they're toeing this line between promoting, you know, industry and regulating, and it's all very tough, um, but they're meeting with Meta, Google, OpenAI, and Anthropic today supposedly to review the framework which is done, and they're gonna get their feedback on it, um. And then it, you know, we'll see what it is, and like I said, it's going to be voluntary, so how do you, how do you enforce that? Emily, thank you for joining us. Please stop writing about AI hacking and go and get some sleep because I know it's been a big two weeks for you. Thank you. The hedge funds, so perhaps you've made enough money to love the term, in which case, good for you. Or perhaps, like me, you followed the exploits of Ax Capital through 7 seasons of billions. Love that show. But there's one big reason that you're able to talk about hedge funds at all. Fortune magazine. Because the term did not exist before it graced the pages of Fortune, legendary Fortune journalist Carol Loomis coined the term in 1966 while profiling the unique strategy of an investor who made his partners millions with speculative techniques for conservative ends. End quote. So a fund that hedges risk, in other words, by buying stocks you think will go up and shorting others you expect or pray will go down. So Carol Loomis became the poet laureate of leveraged commodities. That's quite a title while reporting on Alfred Winslow Jones' managing. Strategy. The headline of the piece was the Jones Nobody keeps up with, but that quickly changed because that issue of Fortune was the one that launched 1000 funds. Wall Street chased Jones' model and used the Loomis label in the process, and 50 years later, was it happily ever after the hedge funds? Well, your answer will vary greatly depending on whether you're Ken Griffin or Leopold Ashenbrenner, whose AI hedge funds recently completely imploded. So that's it. Thank you for watching 14 Daily. I'm Ellie Austin. We'll see you tomorrow.

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