5 Side Hustles You Can Start Today With AI

Artificial intelligence can be a significant income source for people who choose a smart side hustle. AI may still have a long way to go with some tasks, but with others, it can increase productivity and results. And there are a number of companies looking to take advantage of those benefits. The best AI-driven side hustles use the technology as a great equalizer. Small business owners, particularly of established businesses, often use AI in a limited fashion, if at all, says Peter Hansen, director of research and policy analysis with the National Federation of Independent Business (NFIB). Those founders value and emphasize the human connection to their customers. But even if they don’t use it, they recognize AI can help in other areas. Many of those owners don’t have the time to learn the ins and outs of various AI systems. And even companies that are using AI are often not taking full advantage of it. That creates an opening for side hustles that center around AI. “More than three-quarters of businesses already using AI report higher productivity, yet most are still applying it to a narrow set of tasks,” says Simon Worsfold, head of data communications at Intuit QuickBooks, which puts out a monthly Small Business Index exploring market trends. “What is emerging is a large, underserved layer of advisory, implementation, and training services that small businesses need in order to scale their use of AI,” he says. Here are five ideas for AI-focused side hustles that could be lucrative—or even become the foundation of a full-time business. AI Consultant This is a side hustle with multiple branches. Some people are well suited to help people or businesses integrate AI with their calendar, notes, and local files. Others can develop workflow audits that review how a company’s employees work to discover repetitive tasks that the AI could handle. Rates for an AI consultant range from $150–350 per hour for independent workers and people running it as a side hustle. Those can jump as high as $600 per hour if you convert this side hustle into a full-time company. AI Researcher To stay competitive, businesses need to know what their competitors are doing and what’s trending in their industry. That data can be remarkably complex, however, and not every founder or CEO has the time to do the deep dive it requires. Side hustle operators with strong AI skills can use the technology to filter the data into easily digested insights, then sell those as reports, newsletters, or direct subscriptions. You’ll need to carefully identify the best source material and be sure to double check the output, since AI’s comprehension is still flawed. “The AI doesn’t give the correct answer all the time,” says Shaun Ghavami, Founder of 10XBNB, a side hustle-turned-business that now oversees a portfolio of over $100 million of short-term property rentals. “The real skill is in prompting.” GEO (AI Search) Optimization For the past two decades, businesses have hired specialists to help them rank high on search results pages. While that’s still valuable placement, smart business owners are also looking into how they can be among the frequently-cited sources for AI search results. People with a combination of AI and content knowhow can create a side hustle specializing in this. You’ll teach companies the best ways to refine their content and meta tags and improve readability for large language models. If you have the tools to measure analytics, such as Alli AI, Frase, and MarketMuse, you have a better chance of establishing a recurring relationship with those clients, increasing your income and establishing a base should you want to make this more than a side hustle. Content Repurposing (Clipping) Both content creators and businesses regularly churn out videos and stories as promotional tools, or in an effort to be seen as thought leadership experts. Quite often, though, that content needs to be pared down before they’re posted on everything from TikTok to LinkedIn to Instagram. And that takes time, which the companies and content creators may not have to spare. As an AI content repurposing expert, you can transform the long-form content into digestible bites to widen the potential audience in a much shorter period of time. Use tools like CapCut, Canva, and ChatGPT to effortlessly pick and highlight the most relevant parts. Create AI Microtools AI can be intimidating to many people who don’t know where to start, especially when it comes to creating agents or GPTs that are relevant to their needs. A smart side hustle operator, though, knows that you don’t have to be an expert to create your own AI tools. Sites like Gumloop let you visually build workflows that can be run at large scale. You’ll use a drag and drop template that lets you create workflows that do just about anything, from running a search engine optimization audit of a Website to a ranking system for job applicants. You can either custom create those for clients or build general purpose tools and sell them on Etsy or some other marketplace. BY CHRIS MORRIS @MORRISATLARGE

What Burning Man Can Teach Startups About Surviving the AI Age

Turning an idea into a business used to be so costly and time consuming that, when we had thoughts such as, “What if an app could let my neighbors order homemade dinners from each other?”, we rarely took action. Instead, we just waited until someone brought something to market and then told our friends, “That was MY idea!” Now, with AI, you can turn any frustration-induced idea into an app or prototype in a weekend. The barrier to success for startups is no longer execution; it’s ease. How can you succeed if your product can be easily produced by everyone else? The answer may be found right now in Black Rock City. Once a year, tens of thousands of people descend upon the Nevada desert to co-create Burning Man. They produce awe-inspiring art installations, build outrageous “mutant” vehicles, and put on immersive performances guided by principles like gifting, radical self-reliance, radical self-expression, and communal effort. Stanford professor Fred Turner explains how Burning Man provided the “cultural infrastructure” for much of the startup tech industry. Open-source, collaborative, project-based ways of working and utopian dreams dominated the early ethos of companies like Google. AI can’t copy the most important aspect of Burning Man. AI can render a flame-throwing octopus sculpture in thirty seconds, but it can’t render the experience of the guy who spent four months welding it in his garage. The value of Burning Man comes from being there, expressing yourself, building something, meeting people, and contributing. Participation, belonging, and shared experience are hard to duplicate. This suggests three places to look for customer value your competitors can’t duplicate. When technology makes things abundant, look for what becomes scarce. When everyone has a vending machine, fast snacks are no longer a lucrative business. AI didn’t just lower the barrier to entry. It removed it and bulldozed the building behind it. If you want a competitive moat today, allow people to create unique value for themselves and others in unplanned, irreproducible ways. Don’t just ask, “How can we use AI to create more value for our customers?” Ask: “What can our customers create for one another that we can’t, and AI can’t?” My last article explained how The Rocky Horror Picture Show turned audiences into participants and created a 50-plus-year loyalty phenomenon. Burning Man takes the idea even further. “Burners” don’t just enjoy a product. What they create for one another is the product. When technology makes things easy, look for meaningful friction. Ease and value don’t always go together. Burning Man is what happens when nobody optimizes the customer journey. Most founders see a week in the desert hauling water and building shelter and think: how do we remove this pain point? Burners built a culture around refusing to answer that question. You can’t just watch Burning Man. You have to invest heavily in creating it. As much as humans like to make things easy, we also value what’s hard-earned. We invented the car to stop walking, then invented the treadmill so we could walk hard—and post about it. As AI makes more things easier, people will start looking for new hard-earned experiences they can grow from and be recognized for. Before using AI to eliminate effort for your customers, ask whether any of that effort creates mastery, commitment, connection, pride, or a sense of ownership. Eliminate the friction people resent, but amplify the friction they value. When technology makes things predictable, create room for surprise. AI writes in a voice it’s already heard about things that have already happened. That’s why so many AI logos look like they’ve been designed by the same nonexistent designer, and why AI copy reads like it was assembled from other copy that was assembled from other copy. Burning Man has no training data. Nobody can predict what happens next, including the people running it.It’s hard to pre-engineer surprise, but you don’t need to. You just need to create a way for unique individuals (your customers) to contribute meaningfully, create value for one another, and build things you never would have thought to build for them. Here’s the Bottom Line AI makes things easier to build but harder to matter. Your greatest value might not come from what you produce, but from what happens when your customers show up and contribute. BY STEPHANIE DAVIS

Bill Gates Says the AI Era Will Be Turbulent. Here Are 7 Things You Should Do Right Now

Imagine showing up for work one morning and discovering that the task you spent years learning to do can suddenly be done by AI — faster, cheaper, and perhaps even better than you can do it. For millions of people, that scenario may no longer be science fiction. Bill Gates recently published a thought-provoking essay about what he calls the “turbulent AI era.” His message is both optimistic and unsettling: AI has the potential to make life dramatically better, but the transition could be enormously disruptive. Gates believes AI will affect both white- and blue-collar jobs. Unlike previous technological revolutions, the change could happen remarkably quickly. He argues that society needs to start preparing now rather than waiting until millions of workers are displaced. So, what does that mean for you? Here are seven things you can do right now. 1. Stop assuming your job is safe. AI isn’t just coming for repetitive factory jobs. Gates points to sales, customer support, software engineering, paralegal work, data analysis, and other occupations as vulnerable. Take an objective look at what you do every day and ask yourself: How much of this could AI eventually do? 2. Learn AI before you need it. Don’t wait until your boss tells you to start using AI. Experiment with the tools available to you now. The more you understand what AI can and can’t do, the better prepared you’ll be. 3. Use AI to make yourself better. Instead of viewing AI solely as a competitor, make it your assistant. Use it to research, brainstorm, analyze information, automate routine work, and free up your time for higher-value activities. 4. Double down on being human. Gates proposes an intriguing idea he calls “Human Reserved” — work that society deliberately decides should remain in human hands. Empathy, judgment, trust, creativity, leadership, and genuine human connection will become increasingly valuable. Strengthen those skills. 5. Keep learning. The worst strategy in a rapidly changing workplace is standing still. Identify skills that are becoming more valuable in your industry and start developing them before you desperately need them. 6. Help your people prepare. If you’re a leader, don’t pretend nothing is changing. Talk openly with your employees about AI. Give them access to tools and training and involve them in deciding how AI should be used. Your people deserve the opportunity to adapt. 7. Don’t surrender your judgment. AI can produce impressive answers, but that doesn’t mean you should stop thinking. Gates is particularly concerned that overreliance on AI could weaken critical-thinking skills. Use AI to extend your intelligence — not replace it. The AI revolution isn’t something that’s going to happen someday. It’s happening right now. You probably can’t control how quickly AI develops or what your competitors do with it, but you can control how prepared you are. And the time to prepare isn’t after your job changes. It’s before. EXPERT OPINION BY PETER ECONOMY, THE LEADERSHIP GUY @BIZZWRITER

The 1 Big Compensation Decision Founders Should Never Hand to AI

AI has quickly made itself useful across hiring. It can sift through résumés, draft a job description in seconds, and match a role to market benchmarks. For a small company that never had a compensation team, that’s a genuine unlock. But things get thornier when AI has a say in what the actual salaries that companies pay their employees should be. And that’s the bigger question that founders now have to grapple with—whether they should let AI set the number itself. That pressure is only growing, and this year it found a sharp edge in the law. On October 8, 2025, California outlawed a hiring shortcut that employers had leaned on for years. When Governor Gavin Newsom signed SB 642 into law, effective January 1, 2026, the state redefined what a “pay scale” in a job posting is allowed to be: a good-faith estimate of what an employer expects to pay a new hire. The point, as the law firm Morgan Lewis noted, was to stop employers from posting “meaningless pay scales simply to be in compliance.” So the old dodge is dead. Post a range wide enough to protect yourself, say from $80,000 to $200,000, and you’re not being cautious. You’re non-compliant. California isn’t alone, and these rules don’t only apply to big companies. As of early 2026, more than a dozen states now require employers to disclose pay, and the thresholds reach startups, not just giants. In Colorado, a single employee is enough to trigger disclosure, while New York City draws the line at four; states like California, Illinois, and Washington set it at 15. For founders who have always set pay based on their instincts, that guess is now a public document, and the person most likely to read it closely is the employee who already holds the job. And not just them: everyone doing similar work will see the number too, and measure themselves against it. The problem was always there Improvised pay stays invisible while it works, and it works right until two people doing the same job discover they earn thousands apart, or a strong hire leaves for a competitor who bothered to benchmark the role when you didn’t. Jonas P. Johnson, who works on compensation modeling at ERI Economic Research Institute, a firm that has built salary data for more than three decades, says instinct-based pay produces one of two problems, and they aren’t equally fixable. Underpaying firms struggle with turnover, while overpaying firms struggle to keep prices competitive. Most founders assume overpaying is the safe mistake. Johnson says it’s the harder one to escape. Underpaying is the easier hole to climb out of, because raising pay tends to slow the turnover it caused. Overpaying is the real trap, since you can’t take money back once people are earning it. “Employees won’t accept pay cuts,” Johnson says, so a company that has drifted above market has to hold raises below average for years to come back down, all while trimming costs to survive. The case he sees most is the firm that bought out a competitor and inherited its payroll, absorbing someone else’s guesswork and making it their own. How one raise becomes a chain reaction Transparency rules do more than reveal the gap between what you pay and what the market pays. They reveal the distance between your newest hire and your most loyal one, which is the first comparison employees make. The way it unfolds is easy to trace. When market rates climb, you post a higher range to attract someone new, and that range is now visible to the person who has held the job well for three years, and to their peers in similar roles. Fresh money at the top squeezes the space between new and existing staff, and Johnson warns it “can lead to turnover among existing employees unless the organization re-scales the entire salary structure.” A single number in one posting can force you to reopen everyone’s. Compensation professionals call this pay compression, and it hits morale before it shows up in a budget. Jason Greer, an employee and labor relations expert, says a compressed structure creates “zombie employees” who “show up, clock in, and do the minimum.” They haven’t walked out because they have learned what a newer colleague earns and have reduced their effort. His advice is to understand your local market before circumstances force the issue, because “you might be global, but to your employees, you’re local.” Why the average raise misleads you Guesswork is seductive because there’s always a tidy number to reach for. Heading into 2026, most surveys cluster raises between 3.2 and 3.5 percent, down from post-pandemic highs near 4.4 percent. But the average describes almost no one. Some jobs see no salary growth year over year, Johnson says, while others climb 8 percent. Pay tends to lurch rather than climb, and his clearest example comes from the start of the pandemic, when compensation for forklift operators had been flat for years and then jumped 22 percent in two months as supply chains seized. Budget off the blanket figure, and you underpay the very people you can least afford to lose. The one call you shouldn’t hand to AI None of this complexity means a 30-person company needs the machinery of a Fortune 500. A large firm leans on formal pay grades to keep several hundred jobs consistent, because at that scale nobody can price every role by hand. A 40-person company has maybe 20 distinct roles and can price each against the market directly, without collapsing them into bands. The point isn’t to build an elaborate structure, since none of this needs a complicated setup. You just have to know what a job is really worth before putting a number on it. What makes that reachable now is that the grunt work has gotten cheap. Matching your internal roles to market data once meant hiring a consultant for weeks. Today, the same first pass takes about a day using AI tools. But Johnson is clear that speeding up the work does not mean handing over the decision. Software can widen the field of comparable jobs and flag where your pay drifts out of line, yet “all final decisions should be made by humans,” he says, because an AI model will always return a confident number even when the data behind it is too thin to trust. Workforce strategist Terri Gallagher pushes the point even further, arguing that compression isn’t a pay problem at its root, but instead “a workforce strategy issue.” The number you choose shows what you think the work is worth, and no tool can decide that for you. The answer, then, isn’t to keep AI away from pay. Let it do the first pass, gather the comparisons, and show you where you’ve drifted. Then make the final call yourself, because that judgment is the one part of the job that was always yours. The expensive answer For most of the past decade, “we’ll figure out compensation as we grow” was a reasonable thing to say. The cost of having no system stayed hidden; a little attrition here, a bruised ego there, all of it absorbed into the churn of a growing company. Transparency law takes that hiding place away. The guess is a public document now, the compression is plain to the people it costs, and the tools to do the work properly have never been more affordable. A founder who treats all this as a compliance headache will keep patching it one uncomfortable job posting at a time. But the one who treats it as a reason to finally learn what each role is worth will find the law was never the real issue. The real issue was always whether they knew what they were paying for. EXPERT OPINION BY KOLAWOLE ADEBAYO