The Best AI Success Stories Are Sitting on Hard Drives and Have 1 User

I had coffee with my favorite CTO yesterday and he told me about his new AI app. It’s basically a CTO-in-a box. And it’s awesome. And he’s the only one using it. And it’s going to stay that way. Despite my trying to persuade him otherwise. One of the reasons there’s so little proof of the value of AI is that the best, most useful, most ingenious apps actually never leave the creator’s hard drive. In fact, once my friend pointed out what he was doing, I myself realized that most of what I’ve created with AI is available only to me on my hard drive, and moreover, that’s definitely where my best stuff is. In fact, it seems like most of the better “AI apps” aren’t even primarily AI, but AI being implemented, like my CTO friend implemented it, to unlock automation and unstructured data — and ultimately narrative output — in a way that couldn’t be done before. So why is this happening? The Genius of CTO-in-a-Box I’m probably overhyping this because he’s my buddy and he kindly listens to a lot of my BS before it gets to you folks, but my CTO friend’s CTO-in-a-box isn’t anything to eff with. He and I worked shoulder-to-shoulder for years, and together we developed some amazing little features, a few apps, and the tech backbone of a multimillion-dollar business. I say “we” but all I did was dream stuff up with him, vet it, and MVP it out, after which he and his brilliant team coded it. And they got it right the first time every time, and he usually added his own flair to surprise me with some technical trick no one would ever notice but made what we were doing 10 times better under the hood. He left that company not long after I did, and despite my trying to wrangle him into what I was doing, he took another job, to come in and do a technical turnaround on a private equity-purchased startup that had tons of potential but was stagnating. He hadn’t done anything like a turnaround before and I had just finished one. We have coffee every two weeks and so our conversations turned to the science of the turnaround. Then he disappeared for a month, and when we got back together, yesterday, he shocked the hell out of me. “Basically, what I did was take every bit of data, company data, sales data, all the code, all the documentation — they had a lot of ‘stuff’ [his air quotes] just sitting in directories and databases,” he told me. “I slammed it all into a vector database, wrote some code, integrated Claude Code to build some agents and totally write the front end, and now the LLM is like my personal assistant.” He’s underselling it. I know this because of the example he gave me. Builders Gonna Build “We had a sudden spike in resources, so I asked it what was going on, and it brought me to the right section of code that was the problem and hypothesized why, and I fixed it in 30 seconds,” he said. And then he made me jealous. “Oh, it also does all my weekly status reports and my standup agenda and all the reporting I have to do for the ELT and the board,” he continued. “I don’t let it send emails, but it’ll create the draft for me to review with the summary and a link to the report.” “Tell me you built it so anyone can use it,” I said. “Of course,” he responded. “I mean, not for all the outliers, but yeah you could start over and import new data, it knows what it’s getting and what to do with it.” “Tell me it’s self-perpetuating with new data it creates on its own,” I said, “like those email summaries and reports.” He just smiled. “Dude,” I said and threw my hands up. “It’s a CTO-in-a-box. Let me at it.” “No,” he laughed. “It’s staying on my hard drive.” “But you built it like a product.” “Because that’s how I roll.” Then he took a smug sip of his mocha whatever and I couldn’t help not being mad at him. Don’t Be So Quick to Write Off AI I say this as the guy who can’t stop writing off AI. Nah, I’ve been disparaging how we’ve been selling AI for years now, having been building it since 2010, and, in a nascent sense, as far back as 2000. But each time I’ve firebombed today’s AI hype in public, especially generative AI — because that’s the “AI” everyone is familiar with and what 95 percent of people are talking about when they say “AI” — I’ve prefaced my flaming with how amazing the technology actually can be when you know what you’re doing. In the hands of my CTO friend, amazing doesn’t even begin to describe what you can do. For the record, he’s on the uppermost subscription level of at least five different providers, a four-figure-a-month bill footed by his private equity overlords. And he’s aware that he will be squeezed soon. In fact, he said openly, “I got on the gravy train while the platforms are loss-leading.” They’ll price him out, and that’s another reason not to build a public product around it. He doesn’t know the true economics. Do What the CTOs Are Doing Of course, I asked my CTO friend to send me his documentation, because of course he documented it, and I’m building something around content and creators that could use its own CTO-in-a-box. And that got me thinking. Right now, all the coding I’ve done with the AI and the agents and such, it’s all sitting on my hard drive, and like my friend, I’ve built it like a product but I’m the only user in the credentials table. But unlike my friend, I built it like a product because I am indeed thinking of packaging it and selling it as a product down the road. If I could just stop writing for a while and get my brain on it for more than five minutes. Which, in today’s world, actually gets a lot of Claude coding done. It’s the peer review that takes time, if you get me. If I’ve got advice, it’s this. If you want to build something with AI, find the people who are doing amazing things on their hard drive — facing real challenges, solving real problems, and not just leveraging AI to jump on the gravy train. Buy them a mocha whatever and ask them what they’re doing and how they’re doing it. Because the more my CTO friend spoke, the more my vision was clouded by dollar signs. The problem is that for every story like his I hear 100 more stories about chatbot wrappers and unstructured data parsers being sold like they’re magic. Those aren’t being funded anymore, finally. That opens the door for people to wring real value and usage out of this AI nonsense. If you’re a fan of real value and usage, jump on my email list. I try to talk about that as much as possible, whether that’s AI or tech or something else. EXPERT OPINION BY JOE PROCOPIO, FOUNDER, JOEPROCOPIO.COM @JPROCO

Mark Cuban Has 2 Words for People Who Don’t Want to Learn AI

Skims founding partner and sometimes visiting Shark Tank Shark Emma Grede was never an AI skeptic, exactly. In 2023, she offered a cash bonus to her staff for finding creative ways to use AI in their work. But she herself was mostly just using ChatGPT as an occasional replacement for Google search. “I’m using AI like a 42-year-old woman,” she joked in a recent Fortune interview. Then she had former Shark Mark Cuban on her podcast. Turns out the billionaire founder and former Mavs owner has strong words — two, to be exact — for people like Grede who are dragging their feet on experimenting with AI. Talking to Cuban was enough to convince Grede to change her approach. She started Googling class on AI and downloading AI apps immediately. The episode “gave me a new urgency around how I use AI,” she told Fortune. “He gave me a kick.” It might be just the kick you need too. Not learning AI? Mark Cuban says “you’re f***ed” On her podcast, Grede didn’t ask Cuban about AI. She asked him about how to get started with a business idea. But the billionaire entrepreneur insisted that now, there’s no difference between going from idea to execution and utilizing AI. You need the latter to do the former fast and well. “The first thing you have to do is learn AI,” Cuban responded. “Whether it’s ChatGPT, Gemini, Perplexity, Claude, you’ve got to spend tons and tons and tons of time just learning how it works and how to ask it questions.” Noodling around with new tools and asking various AI models questions is how Cuban is spending his time at the moment. And he has no patience for founders and others in business who aren’t doing the same. “What do you say to someone who is like, ‘I don’t like AI. I don’t want any more technology in my life’?” Grede asked. Cuban’s answer was short, punchy, and profane: “You’re f***ed.” Is Mark Cuban right? Cuban went on to explain that the current moment is much like his early career at the dawn of the internet age. New, hugely disruptive technology is rolling out at an incredible rate. Those who don’t run to keep up are going to end up as roadkill. Saying you don’t want to use AI, he says, “is like people saying back in the day, I don’t want to use the PC. I don’t want to use the internet. I don’t need a cellphone, Wi-Fi.” Those businesses died. Is he right in making the comparison? He’s certainly correct that those around you are adopting AI at a rate equal to or greater than the rate at which the internet took off. Harvard researchers have compared recent data on AI usage to government data on the uptake of new technology at the turn of the millennium. They found more people are using AI more quickly these days than people started adopting the internet back then. “The usage rate [for AI] … is actually higher than both personal computers and the internet at the same stage in their product cycles,” the trio of researchers explained to The Harvard Gazette. No one can predict the future. And the breathlessness of some discussions of AI certainly suggest that the hype will exceed the reality in plenty of areas. We may yet witness an AI “trough of disillusionment” or even crash. But the numbers strongly suggest that Mark Cuban is on to something when he says that ignoring AI is just not a viable option. What happened to businesses that ignored the internet? “If you were to go back to 1984 and tell people, ‘Hey, there’s this new thing called the personal computer. I have a crystal ball. Twenty years from now, everybody’s going to have one of these and every single new technological development and every single new product is going to be using it as the base.’ Knowing that now, what would you do differently?” the Harvard researchers ask. “You could make billions and billions of dollars,” they add. According to their data, they say, “it sure looks like generative AI is going to be on that scale,” and “the spoils will go to people who can figure out how to harness it first and best.” How to get started with AI If you’re convinced, how do you start learning AI? Playing around with new tools and technologies as Cuban suggests is certainly a good first step. Elsewhere, Cuban — along with other tech icons like Tim Cook and Bill Gates — has outlined specific ways he’s using AI, which could give you additional ideas. Other AI experts have advice as well. Nvidia CEO Jensen Huang has talked on multiple occasions about how he’s personally experimenting with AI. OpenAI president Greg Brockman has offered advice on honing your AI prompting skills. No one knows exactly how the AI revolution will play out, or even the best way to start to prepare. But even the skeptics should probably heed Mark Cuban’s words and admit that AI is going to change the world. If you stick your head in the sand, you’re doomed. Better start experimenting today so you can be prepared however this thing plays out. EXPERT OPINION BY JESSICA STILLMAN @ENTRYLEVELREBEL

Is the AI Bubble Too Big to Fail?

On Wednesday, analysts bemoaned Nvidia’s lackluster Q2 earnings. The company posted a 56 percent gain in sales, its smallest in more than two years, despite the chipmaker’s positioning as one of the biggest winners of the AI boom. The company’s inability to live up to its expectations has reignited fears of an AI bubble on the precipice of rupture. Despite Silicon Valley throwing hundreds of billions of dollars into its most speculative gamble yet, the revolutionary promises, and more important, profits, of AI have yet to materialize. OpenAI is expected to lose money this year, even as its revenue exceeds a projected $20 billion. Meta’s CFO told investors, “We don’t expect that the genAI work is going to be a meaningful driver of revenue this year or next year,” despite the company dropping upwards of $70 billion on its AI investments this year. A recent MIT study found that U.S. companies have invested between $30 billion and $40 billion into generative AI tools but are seeing “zero return” from AI agents. Some fear that all of this could presage a collapse bigger than the dot-com bust of the early 2000s. As Apollo Global Management’s chief economist warned in a recent investor’s note, big tech firms are driving the market with valuations more bloated than they were in the 1990s. This would be scary for big tech companies—except many of them, according to several researchers who spoke to Inc., are already too big to fail, thanks to how closely the industry has become intertwined with our economy and government. The leading AI companies believe “the only way for this technology to exist is to be as big as possible, and the only way for it to get better is to throw more money at it,” says Catherine Bracy, CEO of the policy and research organization Tech Equity. That need for money and investment has spurred an industry lobbying blitz, pushing everyone from OpenAI CEO Sam Altman to VCs like Andreessen Horowitz into the halls of Congress over the past couple of years. Just earlier this week, The Wall Street Journal reported that Andreessen Horowitz and OpenAI are behind a nascent lobbying campaign through a super PAC network that’s already amassed $100 million to elect AI-friendly candidates. Those beltway relationships appear to be paying off. Currently, more than 30 states offer tax incentives for data center construction. But the booming growth of the industry has been enormously costly, largely owing to the vast amounts of energy needed to run large language models. The Trump administration’s AI Action Plan frames the industry’s growth as essential to “human flourishing” in the U.S. and the country’s continued geopolitical dominance. “We’re now locked into a particular version of the market and the future where all roads lead to big tech,” says Amba Kak, co-executive director of the AI Now Institute, which studies AI development and policy. Indeed, the success of major stock indexes—and perhaps your 401(k)—is resting on the continued growth of AI: Meta, Amazon, and the chipmakers Nvidia and Broadcom have accounted for 60 percent of the S&P 500’s returns this year. But ultimately, in the event of a market reckoning, it’s likely that the biggest companies would remain relatively unscathed. “AI is too big to fail in the United States, both because of how intertwined it has become with the government, and also because of how much AI investment is propping up the stock market and the entire economy,” says Daron Acemoglu, an economist at MIT. When the bubble pops, it’s likely going to be the smallest AI businesses, those riding the AI hype train with products based on existing LLMs, that’ll get wiped out in an eventual rupture. “Those little companies are not going to get bailed out,” he argues. Hardware companies like Nvidia or big tech firms, with diverse revenue streams, are likely to be better insulated from the potential fallout of the bubble popping. As Timnit Gebru, a former Google AI researcher and founder of the Distributed AI Research Institute, puts it, a chipmaker like Nvidia is essentially just selling shovels during a gold rush. “Shovels are still useful with or without the gold rush,” she says. BY SAM BLUM @SAMMBLUM

Why Google’s New AI Image Generator Could Give OpenAI a Run for Its Money

Google just dropped a major update for its AI image generation tech, enabling anyone to generate images with more accurate outcomes. In a blog post, Google revealed Gemini 2.5 Flash Image (also called nano-banana), its latest and greatest AI model for generating and editing images. Google says the new model gives users the ability to blend multiple images into a single image, maintain character consistency across multiple generations, and make more granular tweaks to specific parts of an image. One of the model’s new features is that ability to maintain character consistency, meaning that if you create a specific look for an AI-generated character, the character will maintain that look each time you generate a new image featuring them. “You can now place the same character into different environments,” Google wrote, “showcase a single product from multiple angles in new settings, or generate consistent brand assets, all while preserving the subject.” Gemini 2.5 Flash Image can also make more granular edits to images, like blurring a background, and changing the color of an item of clothing. Another major feature is the ability to fuse multiple images into a single image. Google says this could let people place an object into a room or to restyle an environment with a new color scheme or texture. To demonstrate, Google built a demo in which users can upload a picture of a room, upload images of products that they’d like to see in the room, and then drag the product image to the specific place where they want it to appear in the room. It’s not difficult to imagine people using this feature to see how a new appliance or piece of furniture will look in their home before committing to a purchase. Google also says that Gemini 2.5 Flash Image is particularly adept at sticking to visual templates, such as real estate listing cards, uniform employee badges, and trading cards. This kind of feature could also be used to create thumbnails for YouTube videos. Gemini 2.5 Flash actually debuted on website LMArena last week under the codename nano-banana. LMArena is a platform for evaluating an AI’s performance against other AIs, and big artificial intelligence companies often submit their new models to the site before publicly revealing them. Also of note is Gemini 2.5 Flash Image’s API price. According to Google, the model is priced at $30 per one million output tokens. In comparison, OpenAI’s image-generation API fees cost $40 per one million output tokens, making Google’s offering significantly cheaper. The new model can be used in the Gemini app and in Google AI Studio. BY BEN SHERRY @BENLUCASSHERRY