GEN-Z IN 2024

They make up one-fifth of the labor force, they have strong entrepreneurial inclinations, and they’re not afraid to make their voices heard—especially when they feel they’ve been wronged. The members of the generation born between 1997 and 2012 are coming into their own and changing the face of the workplace in the process. It’s no wonder why, in 2024, we just couldn’t stop talking about Gen-Z. This year, Inc. paid close attention to this cohort of entrepreneurs and employees. We’ve taken notice of their drive for change, as editor-in-chief Mike Hofman sat down with the youngest honorees of this year’s Inc. 5000 to understand how Gen-Z business owners are shaping workplace culture. We’ve also dug into the growing pains Gen- Zers are facing as they step into the corporate world for the first time: why they’re “consciously un-bossing,” getting fired from their first jobs, feeling unsatisfied, and quitting as a result. For these reasons, we’ve shared advice for business owners to better hire—and, critically, retain—their Gen-Z talent. As this generation has increased its buying power, we’ve also paid close attention to its digital habits (as it fueled the seemingly never-ending brat summer) and figured out the ways brands could better market to it, giving in to unhinged content that is decidedly not demure. With 2025 just around the corner, we’ll be keeping an eye on how this generation continues to reshape the working world—and paying close attention to the Gen-Z entrepreneurs who are building some of the most world-changing companies. There’s a lot to look forward to as Gen-Z continues to carve its own path forward. —Rebecca Deczynski, senior editor at Inc.

How Top Cybersecurity Firms Are Scaling Faster and Smarter to Win in 2025

The cybersecurity market continues to take off—and so does the competition. With global spending on information security projected to hit $212 billion by 2025, according to consultancy Gartner—a 15 percent increase from 2024—cybersecurity companies face a relentless battle on two fronts: defending against evolving threats and outpacing rivals to seize market share. The opportunity is massive, but so is the pressure to deliver. In cybersecurity, the buying process is inherently nonlinear. Buying cycles are unpredictable, threats evolve daily, and missed revenue signals can cost millions. For companies chasing growth, IPOs, or exits, the game has changed. To stay ahead, top cybersecurity companies are modernizing go-to-market strategies, zeroing in on high-growth segments, and creating repeatable, predictable wins. As CEO and co-founder of Clari, an enterprise revenue platform, I work alongside Fortune 500 companies—including cybersecurity clients Fortinet and Okta—to drive predictable revenue growth. Here’s how they’re doing it: Scaling revenue growth exponentially In a hyper-competitive market, predictable revenue growth is the result of discipline, data, and decisive action. To achieve this, companies need a reliable revenue baseline to accurately assess the probability of closing a sales opportunity, identify warning signs to weed out no-decision and slipped opportunities early, and follow rigorous forecasting and sales principles to drive more predictable business outcomes. Fortinet, a global cybersecurity leader, tackles this problem head-on by centralizing revenue operations (RevOps) data. The company integrates information from separate platforms—like emails, calls, and calendar invites—into a single system. This unified data creates a shared source of truth, enabling Fortinet to build a consistent revenue process. This isn’t just about centralization, it’s about transformation. Armed with advanced forecasting capabilities and historical trend analysis, Fortinet gains an edge. Automated insights into future performance allow the entire revenue team to take action proactively earlier in the quarter. With this rigor, Fortinet achieved 97 percent forecasting accuracy. Fortinet’s leadership can make confident decisions about resource allocation, capacity planning, and reinvestment. For a market as high-stakes as cybersecurity, this level of precision doesn’t just drive growth—it gives companies a competitive edge. Fortinet has proved that when companies eliminate guesswork and operate with precision, growth becomes scalable and success becomes repeatable. Predictable growth: command and control revenue Before its IPO, Okta had a problem that most high-growth companies face: the chaos of unreliable forecasting. Sales reps relied on best-guess numbers, rolling up inconsistent estimates into leadership reports. This manual, time-consuming system was destined to break under the weight of Okta’s rapid growth—and for a company racing toward an IPO, that level of unpredictability wasn’t an option. Okta’s leadership knew that to sustain their momentum, they needed to overhaul not just their processes, but their entire approach to revenue alignment and execution. They implemented a structured, scalable forecasting framework and brought every critical team—sales, marketing, and customer success—into lockstep. The shift wasn’t just operational; it was cultural. A consistent cadence of pipeline reviews and forecast meetings became the backbone of their revenue strategy, driving collaboration and accountability across the organization. The results were transformative. Okta gained the visibility and consistency needed to navigate its IPO with confidence. And leadership had clarity and trust in their revenue data, empowering teams to make data-driven decisions that balanced short-term priorities with long-term growth. By turning forecasting into a disciplined, cross-functional process, Okta didn’t just solve a pain point—it built a foundation for predictable, scalable growth. The story illustrates that when teams align around a shared revenue strategy, chaos becomes control, and growth becomes achievable. Why operational excellence will define the next cybersecurity leaders The cybersecurity industry is operating under relentless pressure: Threats are evolving, competition is fierce, and the margin for error is razor-thin. Cybersecurity IPO winners have proved that staying ahead requires more than innovation—winning demands operational excellence across the entire revenue team. By unifying data, aligning teams, and modernizing forecasting, they’ve used data and technology to deliver precision. For cybersecurity firms aiming to grow, scale, or go public, the lesson is clear: Operational discipline isn’t a choice—it’s the new standard for success. EXPERT OPINION BY ANDY BYRNE, CEO, CLARI

I Called 1-800-ChatGPT and Talked to the AI Chatbot. It Might Be the Smartest Idea I’ve Seen Yet

This morning, I spent 15 minutes on the phone with ChatGPT. You probably know by now that OpenAI released the ability to dial 1-800-ChatGPT to interact with the chatbot via voice call. And, so, for your sake, after my kids left for school, I sat down and made a phone call. It’s a weird thing to consider, partially because—as a general rule—I try as hard as I can never to talk to anyone on the phone other than my wife or kids. Beyond that, I’d almost always rather communicate by text, or email, or Slack, or anything that doesn’t involve a synchronous voice conversation. Also, I’m not sure the last time I dialed a 1-800 number that wasn’t to call an airline. I’d pretty much assumed they’d all been taken. Though considering the amount of money OpenAI paid to buy the chat.com vanity url, I imagine this was a bit easier. I came prepared with a list of questions to see how the experience is different from all of the other ways you can interact with ChatGPT. On the one hand, it’s a lot less useful than visiting chat.com or using one of the various apps. It’s more of a novelty or party trick that you might pull out because, hey, why not make a phone call to a chatbot just for fun? On the other hand, it works exactly like ChatGPT, except more friendly and more polished than voice mode in the app. I used the iOS 18 feature that lets you record phone calls, and when my iPhone gave the “this call is being recorded” alert, ChatGPT responded, “Great, let’s talk!” Then, I asked what seemed like an obvious question for mid-December: “What are some common things that kids do to try to have a snow day?” “Kids have some classic snow day rituals,” ChatGPT replied. “They might wear their pajamas inside out, flush ice cubes down the toilet, or even sleep with a spoon under their pillow, all in the hopes of a snow day miracle.“ Which, to be fair, is exactly the correct answer. If you had asked any of our four children, they would have given you that answer almost word for word. Of course, you could get the same information via any of the other ways you can interact with ChatGPT, so why a 1-800 phone number? Apparently, a lot of people think it’s a great idea. While OpenAI wouldn’t give specifics, CEO Sam Altman tweeted that “Wow people really love 1-800-CHATGPT lol.” It’s hard to measure the success of a product or feature on the basis of a vague tweet from a CEO, but it’s not surprising to me at all that a lot of top people would want to try this out. Again, even if for no other reason than party trick. I think it’s pretty clear that I am not the audience for this. I have a ChatGPT Plus account, and I regularly use ChatGPT on my iPhone and Mac. I even changed my default search engine in Brave to the ChatGPT extension. I’m not likely to make a phone call to talk to a robot unless it’s just for fun. In fact, I’ve written before that my least favorite thing in the world is when companies force you to have a conversation with a customer service bot instead of just letting you talk to a real human. That’s not fun. Fun, it turns out, is a pretty important part of this—and we’ll come back to it in a minute. One thing, however, did surprise me: ChatGPT is very good at understanding voice prompts. Much better than other voice assistants, at least in my experience with the phone version. Which, honestly, is kind of brilliant actually. It does not seem far-fetched that, over the next few weeks, as people get together for the holidays, someone will have a conversation or ask a question, and someone else will say, “Hey, I know how we can get the answer to that.” How fun will it be at that moment to just dial 1-800-ChatGPT? If you do, you’ll be demoing ChatGPT to a bunch of people who have probably heard of the chatbot but have never used it in any meaningful way. This is why this is so brilliant. It’s fun, and it reduces the friction involved with downloading an app or navigating to a website and creating an account. In that sense, it’s exposing an entirely new audience to ChatGPT, in a fun and accessible way. That’s one of the smartest ideas I’ve seen yet. EXPERT OPINION BY JASON ATEN, TECH COLUMNIST @JASONATEN

Gen-Zers Are Big on Side Hustles, and They’re Using AI to Juggle It All

The gig economy is still very much alive, thanks in part to Gen-Z and Millennials. A new survey from Intuit finds that nearly two-thirds of people between the ages of 18 and 35 have either started or plan to launch a side hustle. And, increasingly, they’re leaning on artificial intelligence to do so. Gen-Zers are opting for a more entrepreneurial approach to their careers. And this, writes Intuit, represents “a material shift in how younger generations approach work, purpose, and financial independence.” Gen-Zers and Millennials have a strong desire to be their own boss, according to the survey. Nearly half of the 1,000 people Intuit spoke with said they wanted to be in charge of their own destiny. Another 42 percent said they were pursuing their passions with these side gigs. The flexibility of these jobs and the chance to build something personal and unique were also key motivators. Gen Zers’ status as digital natives, the first generation to grow up with the internet as a part of daily life, is a big part of the embrace of side gigs as well. Some 80 percent of Gen-Z business owners started their businesses online or had a mobile component. Social media is also a tool they’re employing, with 44 percent relying on platforms, including Instagram and TikTok, to market their side hustle’s services and raise brand awareness. “There’s an entire cultural renaissance happening on social media where creators, business owners and side hustlers are finding their target audience, customer or next gig all in the palm of their hand whenever or wherever they decide to work,” Intuit’s Consumer Trend Expert Marissa Cazem. The side hustles, for now, are being run alongside a regular job. And while 65 percent of those surveyed who are operating one currently say they plan to continue doing so in 2025, figuring out the timing remains the biggest challenge. That’s where AI comes in. The advances the technology offers in reducing the time required for certain tasks has been a boon for Gen-Zers. They are using AI for things like content creation, customer service, and even logo creation and web design. Some Gen-Z founders are even leaning on AI to exit their side hustles. In 2018, Ben Zogby launched HighStrike, which offers educational resources and webinars to help people learn how to invest. He worked on the business during nights and weekends after finishing his 9-to-5 engineering job. Eventually, it found an audience—and earlier this year, the 27-year-old Bostonian sold the business for $1.8 million. Zogby was able to exit for that impressive amount thanks to Flippa, an online platform that uses AI to connect founders with potential buyers, suggesting valuations in as little as 30 minutes. The tool’s large database of buyers also let Zogby locate targeted bidders, saving the time that might otherwise have gone into looking for the right person to buy the business. In the end, HighStrike found itself the subject of a bidding war, which resulted in the $1.8 million exit. Just 3 percent of Gen-Z workers with side gigs say they have failed. Most pivot, Intuit said, when things aren’t working. The gigs can be lucrative, as well. On average, side hustles are profitable after three to six months. And a separate study from Bankrate found the average income of these gigs is $891 per month. “Gen Z and millennials are reshaping the economic landscape,” wrote Cazem of Intuit. “They’re not just participating in the gig economy—they’re leading it, armed with digital tools, entrepreneurial spirit, and a drive for autonomy.” BY CHRIS MORRIS @MORRISATLARGE

Why Microsoft’s New AI May Speed Up Your Company’s Use of New Technology

While businesses embrace AI systems like OpenAI’s ChatGPT or Google’s Gemini, keen to reap the money- or time-saving benefits they can offer, it’s worth remembering that the technology requires vast, often pricey computer resources. This means companies that want to run their own custom AI systems either have to install expensive facilities, or access a third party’s AI via the cloud—a process that can be insecure. Enter Microsoft’s new Phi-4 AI, a much smaller AI model, technologically speaking, than its big name rivals. But though Phi is small, it’s still mighty: data show it performs as well as, if not outperforms the bigger AIs, news site VentureBeat reports. As VentureBeat notes, enterprises that are deploying AI solutions to help streamline their company’s costs, or turbo-boost worker productivity, can face high bills for the computer and energy resources needed to run conventional “big” AI models. As VentureBeat says, “many organizations have hesitated to fully embrace” large AI models due to the cost.” But Microsoft’s new Phi-4 doesn’t need such large technological systems, and could even bring cutting-edge AI capabilities within reach of mid-sized companies, or non-tech outfits that lack big IT budgets. As well as being small, data on how Phi-4 works show it’s really good at math problems, making it a promising tool for use in research, engineering problem-solving and financial modeling, and similar tasks that smaller companies could tackle with a little AI help. Why else would a smaller company embrace a small AI like Phi-4? A recent report in the Economist offered a surprising reason. While fast-developing AI may be considered a threat to some enterprises, since market-leading models are already capable of replicating—perhaps for free—the niche capabilities some companies sell as their core business. While that threatens their future profitability, other enterprises may find benefits to embracing the tech early and innovatively. The publication cites an AI-boosted success at the translation app Duolingo. The app’s core language-learning lessons can be delivered by a chatbot like ChatGPT for free, potentially casting a shadow on Duolingo’s future. But the company leaped to embrace AI, launching an souped-up video chatbot to let language learners practice speaking and getting feedback on their efforts. They even used this AI avatar as part of a recent financial call with investors. The AI delivered Duolingo’s quarterly results—to critical acclaim. So how exactly does Phi-4 differ from, say, a large AI model like Google’s Gemini and why should you care? It’s a question of scale. As VentureBeat explains, models like Gemini can have hundreds of billions—or maybe trillions—of parameters built into their algorithms. These parameters get subtly tweaked when a chatbot AI is “trained” using real-world data. The industry has been advancing on the general principle that bigger is better, with more parameters in the model apparently equating to more sophisticated answers from the chatbot when users query it. But a huge database of parameters needs giant server-scale computers for storage, and countless expensive AI processing chips to trawl through the data when the AI is queried or being trained with new information. To give a sense of the scale involved, Google and Microsoft have said their next-gen AI systems will need $100 billion-dollar investments in the hardware and software. But Phi-4 has just 14 billion parameters in its model, making it much more reasonably sized, so it could be run on a typical server that’s affordable for much smaller companies—enterprises that want to run tailor-made AI systems like this under their own control, to prevent sensitive company info leaking out when using a cloud-based AI service. Recently some AI companies, like OpenAI, seem to have stalled a little in pushing for ever-bigger next-generation AI models. So it’s possible that Microsoft’s Phi-4 model shows that in some AI matters, size doesn’t really matter—what makes it good for business is how your company might use it. BY KIT EATON @KITEATON

This Futurist Predicts a Coming ‘Living Intelligence’ and AI Supercycle

Recent advancements in artificial intelligence hold immense disruptive potential for businesses big and small. But Amy Webb, a futurist and NYU Stern School of Business professor, says AI isn’t the only transformative technology that businesses need to prepare for. In a new report, published by Webb’s Future Today Institute, she predicts the convergence of three technologies. Artificial intelligence together with advanced sensors and bioengineering will create what’s known as “living intelligence” that could drive a supercycle of exponential growth and disruption across multiple industries. “Some companies are going to miss this,” Webb says. “They’re going to laser focus on AI, forget about everything else that’s happening, and find out that they are disrupted again earlier than they thought they would.” A ‘Cambrian Explosion’ of sensors will feed AI Webb refers to AI as “the foundation” and “everything engine” that will power the living intelligence technology supercycle. The exponential costs of computing to train large language models, the report also notes, are driving the formation of small language models that use less, but more focused, data. Providing some of that data will be a “Cambrian Explosion” of advanced sensors, notes the report, referring to a period of rapid evolutionary development on Earth more than 500 million years ago. Webb anticipates that these omnipresent sensors will feed data to information-hungry AI models. “As AI systems increasingly demand diverse data types, especially sensory and visual inputs, large language models must incorporate these inputs into their training or risk hitting performance ceilings,” the report reads. “Companies have realized that they need to invent new devices in order to acquire even more data to train AI.” Webb anticipates personalized data, particularly from wearable sensors, will lead to the creation of personalized AI and “large action models” that predict actions, rather than words. This extends to businesses and governments, as well as individuals, and Webb anticipates these models interacting with one another “with varying degrees of success.” The third technology that Webb anticipates shaping the supercycle is bioengineering. Its futuristic possible applications include computers made of organic tissue, such as brain cells. This so-called organoid intelligence may sound like science fiction—and for the most part today, it is—but there are already examples of AI revolutionizing various scientific fields including chemical engineering and biotech through more immediate applications like research in drug discovery and interaction. In fact, the scientists who won the Nobel prize in chemistry this year were recognized for applying artificial intelligence to the design and prediction of novel proteins. What it means for businesses Living intelligence may not seem applicable for every business—after all, a local retail shop, restaurant, or services business may not seem to have much to do with bioengineering, sensors, and AI. But Webb says that even small and medium-size businesses can gain from harnessing “living intelligence.” For example, a hypothetical shoe manufacturer could feel its impact in everything from materials sourcing to the ever-increasing pace of very fast fashion. “It means that materials will get sourced in other places, if not by that manufacturer, then by somebody else,” she says. “It accelerates a lot of the existing functions of businesses.” Future-proofing for living intelligence Webb says an easy first step for leaders and entrepreneurs hoping to prep for change is to map out their value network, or the web of relationships from suppliers and distributors to consumers and accountants that help a company run. “When that value network is healthy, everybody is generating value together,” she says. Second, she advises entrepreneurs to “commit to learning” about the coming wave of innovation and how it could intersect with their businesses. “Now is a time for every single person in every business to just get a minimal amount of education on what all of these technologies are, what they aren’t, what it means when they come together and combine,” she says. “It’ll help everybody make decisions more easily when the time comes.” Finally, she urges companies large and small to plan for the future by mapping out where they’d like to see their company—and reverse engineer a strategy for getting there. “I know that’s tough. They’re just trying to keep the lights on or go quarter by quarter,” she says. “Every company should develop capabilities and strategic foresight and figure out where they want to be and reverse engineer that back to the present.”

Exclusive: MasterClass Is Introducing AI Mentors, Including a Mark Cuban Chatbot. Any Questions?

MasterClass is bringing its famous teachers into the AI arena. The online learning platform known for its wide variety of celebrity instructors is launching MasterClass On Call, a standalone product that will allow customers to chat with AI-powered duplicates of the platform’s teachers. The cost will be $10 per month or $84 per year. MasterClass founder and CEO David Rogier says the company has been experimenting with the concept of AI versions of its instructors since the launch of OpenAI’s GPT-3 in 2022. He sees the technology as the key to unlocking a feature that MasterClass customers have been requesting for years: the ability to ask its celebrity instructors for advice. Big names like Ray Dalio, Richard Branson, and yes, Mark Cuban, have already inked deals to collaborate with MasterClass on these AI personas. With the rise of generative AI, Rogier says a shift toward on-demand learning is underway. “If I’m negotiating a business deal, I need advice right now,” he says. “I don’t want to sit through an eight-hour class. Just tell me what to do.” Subscribers to MasterClass On Call will gain unlimited, on-demand access to a collection of AI personas designed to be artificial mentors. For example, Rogier says that aspiring entrepreneurs could ask Cuban’s AI to help improve a pitch and role-play as a potential investor. Cuban said in a statement that the new product is “going to be an important tool for entrepreneurs and something I’m excited to be a part of.” In an exclusive demo, Inc. got access to AI versions of sleep expert Matt Walker and Black Swan Group founder and former FBI hostage negotiator Chris Voss, the first two personas currently available in the public beta. The AI voices are remarkably similar to their human counterparts, with natural-sounding cadence and fast response times. When asked for help with a hypothetical salary negotiation, the AI-Voss discussed how to approach the conversation, provided tips on how to strike a balance between confidence and humility, and drafted an initial outreach email. Future updates will bring new in-development personas, enable the AI mentors to remember previous conversations, and give users the ability to upload documents (like pitch decks) for the AI instructors to review. Creating these AI mentors is no easy feat. MasterClass chief technology officer Mandar Bapaye says that the company links together “an orchestra of multiple AI models” to handle individual components, like providing the mentors’ knowledge base or transforming text into speech. The knowledge model is trained on information contained in the mentors’ already-existing MasterClass courses, along with a curated selection of writings and audio recordings. In addition, MasterClass holds extensive interviews with mentors to gather both voice samples and data regarding how they respond to a wide variety of questions. Mentors also give periodic feedback to continuously improve the AI’s performance, like choosing which of two responses to the same question is more accurate to the advice the mentor would actually give. When MasterClass began internal tests of On Call, Rogier was surprised by how comfortable people were talking to the AI mentors. Early testers felt more comfortable sharing with the AI because they didn’t feel any judgement or pressure to impress anyone. They were empowered to ask the “dumb questions” they might be embarrassed to ask otherwise, says Rogier. MasterClass On Call is now available in beta with access to Voss’s and Walker’s AI personas. More mentors, including fashion designer and Queer Eye style expert Tan France, superstar chef Gordon Ramsay, and legendary feminist writer Gloria Steinem are expected to be added over the coming months. BY BEN SHERRY, STAFF REPORTER @BENLUCASSHERRY

OpenAI Just Released Its AI Video Generator, Sora

After months of anticipation, OpenAI has released Sora, its first-ever AI model designed for text-to-video and image-to-video generation. In a live streamed video presentation, OpenAI CEO Sam Altman announced the model’s launch, available now at Sora.com. The release of Sora is arguably OpenAI’s biggest launch of 2024—one the company’s been teasing since first revealing the model in February. Only a select number of customers have had access to Sora since then, such as Toys “R” Us, which debuted the first Sora-created ad in June. In an early review, technology influencer Marques Brownlee called Sora “horrifying and inspiring at the same time.” Sora users will be able to generate video in different resolutions, from 480p (SD) to 1,080p (HD), with higher resolutions taking more time to generate. The size dimensions, length, and speed of the video can also be customized. In addition, users will be able to see other people’s AI-video creations and then remix or alter them. In an example, Brownlee successfully altered a video of a house on a cliff to add a golf course to the background. Users will also be able to upload images and ask Sora to turn them into videos. Brownlee says he found the most success by generating images with OpenAI’s Dall-E, and then uploading them to Sora. Brownlee says there are still some major areas where Sora isn’t ready yet. The model struggles with object permanence—with objects often blipping into and out of existence—and it hasn’t quite figured out how to flawlessly recreate physics. The videos also don’t currently include sound of any kind. As for how this first version of Sora can best be used commercially, Brownlee suggests using the model to create abstract videos and title designs. Sora can generate incredibly detailed textures and complex patterns, so it’s especially good at generating the kind of eye-catching abstracts that can often be found on modern websites. Plus, Brownlee says Sora can be quite accurate at creating titles or logos when given specific words to recreate. Altman says that Sora will be available for users in the United States later today, but access in the U.K. and most of Europe will take some time. According to Sora’s product page, ChatGPT Plus subscribers, who pay $20 per month, will be able to generate 50 videos per month with a 720p resolution and a maximum length of five seconds. ChatGPT Pro subscribers, who pay $200 per month, will be able to generate unlimited videos, and make videos with resolutions of 1,080p and a maximum length of 20 seconds. Pro subscribers will also be able to download videos without a watermark.

How Is Using Generative AI Not Considered Theft?

Over the course of 2024, I put everything I’ve ever written behind a paywall. It’s not something I wanted to do – it’s something I had to do to protect the value of what I write. I have nightmares about some joker in a basement somewhere using my “vibe” to sell crypto scams to old folks. This isn’t (total) hyperbole. What I just described is merely the worst-case scenario of a very common phenomenon with generative AI, and one that we’re all kind of sweeping under the proverbial rug. I know this because I was working with NLG and generative AI as far back as 2011, before AI ethics were even a thing, and even then I could smell trouble on the horizon. Well, here comes 2025 and here comes trouble. See, a lot of LLMs were created during a wild west period of scraping websites for content without permission. Precious little of that was properly verified, let alone properly attributed. So every time you use generative AI, no matter how altruistic your initiative, you’re running the risk of stealing from other people—writers, designers, musicians, coders, attorneys, et al.—to produce information that may also end up being completely inaccurate. It’s fine until you get caught, right? And honestly, what are the odds that massive intellectual theft is going to get traced back to you? I mean, everybody’s doing it, right? Well, I believe businesses are quickly approaching the not-so-fine line between ethics and penalties when it comes to using generative AI. So if you’re using generative AI to support your business, it’s time to decide whether or not it’s worth it. Google Doesn’t Like Crap Content Regardless of how it’s made, Google is starting to get a little more serious about parasite SEO content – websites that host garbage clickbait content to boost SEO juice, like a sports website running unrelated product reviews. A lot of that content now is primarily being produced by generative AI. In fact, that recent article above (from The Verge) references the case of Sports Illustrated getting caught last year using generative AI. But as I pointed out when I wrote about it at the time, while everyone was (rightfully) blasting SI for using AI, they were missing the point. SI was using AI primarily to write product reviews unrelated to its content, and this was a content scheme it had been employing for much longer than it had been using AI to create said content. So why does Google care now? Enough to inflict major search engine setbacks? It’s Not About the Starving Artists Either Yeah, suck it up, writer-boy! You should be grateful that you get to clickity-clack on the keyboard! Except the problem is bigger than artists. In an article I wrote about how using generative AI works against you, I made a tacit distinction between people using AI as a helper tool and using it to imitate the work of a reviewer that’s actually used the product. Because the latter is more than theft – it’s also lying. And maybe light fraud. But even if you’re using ChatGPT to perfect a cover letter for a job you really need, that doesn’t mean the crime – or transaction, I guess – is victimless. I’ve had offers made to me to scrape all my content and turn it into some kind of advice-slinging Joe-bot. None of them were going to make me rich, or even slightly offset the revenue I’m making from various publishers or readers of my (now) private newsletter. What I’m saying is, it exposes the age-old advice warning: You get what you pay for. So even if you’re using generative AI as a tool, even for the most altruistic reasons – and let’s face it, most folks are just trying to make a buck with it – there’s still a very good chance you’re committing theft, as well as a 100 percent chance that you’re getting only a cheap derivative of someone else’s work. It’s why all my content is behind a paywall now. So it’s not just an ethical quagmire – it’s also a poor value proposition. Free AI Is a Myth There’s no such thing as a free lunch, even a free artificial lunch. And this is where I get speculative and conspiratorial. You might be paying pennies or dimes for access to someone else’s processing power and words, but believe me, the proprietors of that power and those words still want your money. Now, let’s talk about Apple and its 30 percent cut of mobile app revenue. Yeah, it was ridiculously cheap to set up a developer license to get our mobile apps onto Apple’s storefront. They want us to do that, they welcome our business. But of course, anything we create and pump out the other end is going to be subject to almost a third of our revenue going back to Apple. Try not paying that. Ask Epic Games about it. So what happens when these ethical AI problems become capital P Problems? Today, as you read this, there are already various lawsuits underway against proprietors of AI for massive theft (allegedly). And it appears that at least a handful of these are going to be winnable. On top of that, an AI Wall is approaching, which is basically a law of diminishing returns on adding any more data to AI datasets because of limits on processing power and, well, a general lack of demand for more complex logic. And if you want to get super conspiratorial, there’s the spooky case of certain names crashing ChatGPT. The reason why is still under speculation, but it’s clear there is some privacy monkey business going on behind the wizard’s curtain. What do you think happens when the resulting revenue problems hit OpenAI, Anthropic, Google, Amazon, and so on? My guess is that it’s going to severely impact whatever the end users are using the generative AI for – and I’ll bet the language to be able to impact the end use is already somewhere in those wordy licensing agreements that those end users glossed over (if they read them at all). You get. What. You pay. For. Look, people like me (and please join my email list to follow along) have long pointed at crypto and said, “That’s cool and all, but it’s not money.” Now it’s time to point at generative AI magic and say, “That’s really neat, but it’s stealing.” And eventually, someone is going to have to pay the price. Don’t let it be you. EXPERT OPINION BY JOE PROCOPIO, FOUNDER, JOEPROCOPIO.COM @JPROCO

AI’s role in scientific research is evolving from a tool to a primary driver of discovery.

Last month, the scientific community experienced a groundbreaking moment with the announcement of the 2024 Nobel Prizes in physics and chemistry. In an unprecedented outcome, both prizes were awarded for achievements involving artificial intelligence—signaling the beginning of an AI-driven era in scientific discovery. This historic event not only honors the visionary minds behind these innovations but also indicates a profound shift: AI is transitioning from being a mere tool to becoming the true driver of discovery itself. Nobel Prize in Physics: Neural networks propel AI revolution The Nobel Prize in physics was awarded to Geoffrey Hinton, often referred to as the “Godfather of Artificial Intelligence,” and John Hopfield. In the 1980s, these pioneers laid the foundation for artificial neural networks—mathematical systems inspired by the human brain. Hopfield designed a network capable of storing and reconstructing complex patterns, which Hinton subsequently advanced. Hinton’s application of the Boltzmann machine enabled feature detection and automated learning, forming the backbone of modern AI technologies, including systems like ChatGPT. Nobel Prize in Chemistry: AI unlocks the protein folding mystery The Nobel Prize in chemistry was awarded to Demis Hassabis, John Jumper, and David Baker for their pioneering use of AI in protein research. In 2020, Hassabis and Jumper developed AlphaFold2, an AI model that solved a 50-year-old challenge: predicting protein structures. This model can now predict the structure of approximately 200 million proteins and is utilized by researchers in 190 countries for drug development, antibiotic resistance studies, and the creation of enzymes to break down plastic. Baker expanded on this by using AI to design entirely new proteins, paving the way for applications in drug development, vaccines, nanomaterials, and microscopic sensors. AI: The new discoverer in scientific advancement This year’s Nobel Prizes in physics and chemistry represent a pivotal moment, elevating AI from a supporting role to a primary force in scientific progress. The breakthroughs in physics, chemistry, and biology that led to these awards were made possible due to neural networks and advanced machine learning tools—not just through human ingenuity. The combined efforts of Hinton, Hopfield, Hassabis, Jumper, and Baker signify a significant transformation in how scientific research is conducted. The traditional perception of slow, meticulous experiments has shifted to AI-driven acceleration, where new insights are uncovered at speeds never before imagined. As technology continues to evolve, science may be entering an era where an AI system itself could win a Nobel Prize—not just the individuals who developed it. Startups harness AI potential to shape industries This transformation goes beyond academia. Major corporations are now putting AI at the core of their scientific efforts. Recently, pharmaceutical giant Eli Lilly appointed Thomas Fuchs as its first AI chief, indicating a new direction for the entire industry. Startups, too, are leveraging AI’s transformative capabilities. A prime example is Xaira, Nobel laureate David Baker’s new venture, which recently secured a billion-dollar investment to commercialize his discoveries. While this level of funding blurs the line between startup and corporation, it highlights AI’s enormous potential for scientific innovation and entrepreneurship. Another example is Somite.ai, the my company, which developed the DeltaStem platform—named after AlphaFold, the system that earned Hassabis and Jumper their Nobel Prize. Somite.ai’s platform trains foundational models to predict intercellular communication and cell differentiation, enabling the discovery and optimization of novel therapies. It also generates vast amounts of biological data, driving further scientific breakthroughs with the ultimate goal of developing treatments that could potentially cure tens of millions of people. Indeed, the future of medicine lies at the intersection of artificial intelligence and biology. But perhaps more profoundly, the very essence of “discovery” is being redefined. AI is no longer just assisting human scientists—it may just yet become the scientist, pushing the boundaries of what was once thought to be solely within human reach. As this AI-driven revolution gathers momentum, the future holds limitless potential for companies and startups to reshape industries and improve human lives. EXPERT OPINION BY MICHA BREAKSTONE, FOUNDER AND CEO OF SOMITE.AI @MICHABREAKSTONE

Here Are the Big 2025 Predictions for AI, From a CEO Who Was Right About This Year’s Developments

It’s that time of year, when tech luminaries offer thoughts on where innovations will take us in the year ahead, and Ai promises to be a driving force. Back in December 2023, tech luminary Bill Gates made a bold prediction about how AI would advance in 2024, guessing that “we are 18-24 months away from significant levels of AI use by the general population.” Gates was largely correct, as the explosive growth of ChatGPT shows, while Apple, Google and Microsoft integrate AI into their consumer- and business-centric tools. Looking to 2025, another AI executive with an even more impressive track record has made his forecast, with some startling surprise forecasts. Clem Delangue, CEO of Hugging Face, an AI-development platform and user community used by millions of developers and big names like Intel and Qualcomm, expects that we’ll see “the first major public protest related to AI” next year. Delangue published his predictions as a list of bullet-points on his LinkedIn page (almost as if an AI had written them). While he doesn’t detail his thoughts on a very human response to supercharged computing capabilities, based on recent controversies swirling around AI adoption, the pushback could be about anything from AI stealing jobs en masse—perhaps in the style of the Occupy Wall St protests—to inappropriate use of AI tech by police, government bodies or health care systems. Delangue also predicts that a “big company will see its market cap divided by two or more because of AI,” implying AI breakthroughs will suddenly render obsolete some core tech or core business philosophy of a major corporation—perhaps in the way that the arrival of the internet hit newspapers’ core print advertising revenue business model. The third prediction is interesting because it crosses from software to hardware: “at least 100,000 personal AI robots will be ordered,” Delangue said. This is right in line with AI robot developments from companies like Tesla and Figure. It also tracks with pronouncements from Elon Musk about humanoid robots, including his own plans to put them to work on Tesla production lines. Delangue also predicted there will be AI breakthroughs in biology and chemistry—resonating with research uses of AI for tasks like drug molecule discovery—and that China will “start to lead the AI race,” a fact that may interest certain concerned parties, like the U.S. government. Lastly, Delangue said the user base of his own company is likely to rise to 15 million “AI builders,” up from this year’s tally of 7 million users. Before you dismiss these predictions as entrepreneurial hucksterism, it’s worth noting that many of Delangue’s AI predictions for this year were accurate, including rising general awareness of the monetary and environmental costs of developing better AI models. Delangue’s musings could also be seen as a useful weather vane for how changing AI tech in 2025 may impact your personal digital life, as well as the technology applied in your company. If your company has been slow to embrace AI tech, this is another reminder that the AI wave is already washing over us, and maybe it’s time to catch up. At the very least, you should maybe try to ensure that it’s not your firm that’s the cause of the first mass public protests against AI tech. BY KIT EATON @KITEATON

Intel Just Forced Out Its CEO. It’s a Brutal Lesson Every Leader Should Learn

Pat Gelsinger was supposed to save Intel. That was the promise when the company named him to the top job back in February of 2021. A respected CEO and former Intel engineer, Gelsinger checked all the boxes and showed up with a plan to restore the company to its former glory. Not long after he took over as CEO, Gelisnger told an audience his thoughts about the company’s focus moving forward: “We’re bringing back the execution discipline of Intel. I call it the Grovian culture that we do what we say we will do. That we have that confidence in our execution. That our teams are fired up. That we said we’re going to do x, we’re going to 1.1x, every time that we make a commitment. That’s the Intel culture that we are bringing back.” I don’t think anyone would disagree that Intel’s culture was a problem. And, I don’t think anyone would disagree that if there was anyone who understood the culture of Intel, it was Gelsinger. He spent a good part of his career at Intel and—notably—was the architect of the 80486 processor. He had also come as a highly respected CEO, having been voted the best tech CEO while he was at VMware. If there was a company more in need of being saved than Intel, I can’t think of what it could be. Under Gelisnger’s predecessors, the company had faced major delays in its advanced chip processes. It had also fallen far behind its biggest competitors, especially TSMC However, Gelsigner did not turn the company around. In fact, it’s not clear whether Gelsigner has succeeded in any meaningful way in restoring the elusive “Grovian culture,” but I’m also not sure it really matters. Instead, on Monday, the company announced he was out after the Board grew impatient with his plan to turn around the iconic chip manufacturer. According to Bloomberg, Intel’s Directors gave him the option of retiring or being fired. It’s a dramatic, though not altogether surprising, turn of events for a CEO who—for a number of reasons—couldn’t live up to the promise of fixing a company where he had spent most of his career. Look, I have no idea whether Gelsigner was the right person for the job, though it is hard to imagine someone with a better overall resume. When he arrived, it definitely seemed like it. Instead, he saw the company fall further behind, culminating in its removal from the Dow Jones stock index after 25 years. Today, the company’s market cap is less than half what it was when Gelsinger took over, while, at the same time, its biggest competitors have skyrocketed. Nvidia, for example, was worth $350 billion the day Gelisnger became CEO. Today, it’s worth $3.3 trillion. It seems, from the outside, like Intel is in a very messy place. It’s a company that, for almost every business reason, shouldn’t exist. Right now, the main rationale for keeping Intel afloat seems to be that it’s critical to national security to have an American company making computer chips. I think that’s certainly true, but it’s just not clear that Intel is going to be that company. I’m not sure that Intel can—or should be saved, but that’s not the point. The point—and the real lesson here—is that it really doesn’t matter if you keep your promises if you make the wrong promises in the first place. Just doing what you say you’ll do isn’t actually enough. It’s equally important that you be doing the right thing. You have to be doing something worth doing. Or, said another way, you don’t get bonus points for keeping the wrong promises. EXPERT OPINION BY JASON ATEN, TECH COLUMNIST @JASONATEN

5 High-ROI Marketing Strategies for Today’s Evolving Landscape

In today’s evolving business landscape, the most effective marketing strategies are shifting, with more companies relying on personal connections in addition to digital platforms and AI. Recently, Inc. Editor-in-Chief Mike Hofman queried Inc. 5000 Community members on their best marketing channels. Generally speaking, founders said the highest-ROI now comes from methods that prioritize authenticity, personal recommendations, and partnerships over flashy ad campaigns. For some, platforms like LinkedIn have proved to be key drivers of business growth, while others are leaning into content marketing, podcasts, or even the boost of being featured in Inc. Honorees also reported that relationships and credibility outweigh traditional advertising in a crowded marketplace. For many Inc. 5000 honorees, referrals, whether from personal connections or online networks, have become indispensable marketing strategies. Below, a few of our business leaders share insights on what’s working for them: “Relationships are the heartbeat of trust, communication, and execution.” — Jennifer (Takenaka) Schielke, CEO, Summit Group Solutions “Referrals haven’t been a sustainable effort for me. Leveraging my Inc. Masters articles with my podcast has led to speaking invitations.” — Gina Anderson, Co-Fonder, Luma “Combined partnerships offer full lifecycle solutions, so that potential clients do not have to shop each piece independently. Our partners in two such efforts are transparent with each other. We realize that by giving potential clients an a la carte approach, full lifecycle services have a much better ROI.” — Beth Maser, CEO, History Associates Incorporated “Referral, and our experiential marketing events.” — Natasha Miller, Founder, Entire Productions “For me, it’s been leveraging my Inc. Masters articles with my podcast to get invites to speaking events.” — Gina Anderson, Co-Founder, Luma “Our highest return has come from formatting done by a proposal writing service, LinkedIn strategy engagement, and Inc. Masters features. Additionally, partnerships that offer full lifecycle solutions have a much better ROI than marketing campaigns or social media.” — Paul L. Gunn Jr., CEO, KUOG “LinkedIn organic networking and account-based engagement is our highest ROI channel right now.” — Lisa Larson-Kelley, CEO, Quantious BY MARLI GUZZETTA