For most of modern history, every major technological disruption has come with the same reassurance: we have been here before. The Industrial Revolution displaced physical labor, but it also created factories, industries and entirely new categories of work. Computers eliminated some jobs while creating software developers, IT departments, digital marketers and an information economy. The internet disrupted everything from newspapers to retail, while simultaneously creating businesses and professions we could barely have imagined a generation earlier. So when concerns about artificial intelligence and jobs arise, it is tempting to reach for the same familiar conclusion: technology changes work, people adapt, new industries emerge and ultimately we create something new. But on August 26, 2026, Bill Gates published an essay on GatesNotes titled “The Turbulent AI Era Is Here. The Choices We Make Now Are Critical .” In it, Gates makes a much more provocative argument about the moment we are entering. He warns that AI is not simply another chapter in the long history of technological disruption and that society is not adequately preparing for the transition ahead. One section of his essay is titled with five words that immediately caught my attention: “This time really is different.” That idea is the starting point for this essay. What follows is not an attempt to predict the future with certainty, nor do I agree automatically with every policy solution Gates proposes. Rather, his essay raises a set of questions that I believe business leaders, policymakers, educators, workforce leaders and communities need to wrestle with now:
Recently, I had the privilege of testifying before the U.S. Senate about artificial intelligence and its transformative impact on the modern workforce. It was an opportunity to discuss an issue that is quickly becoming one of the defining economic questions of our time: What is AI actually going to do to jobs? The conversation around artificial intelligence often gravitates toward extremes. On one side is the promise that AI will dramatically increase productivity and create entirely new industries. On the other is the fear that automation will eliminate millions of jobs. The reality emerging from the data is more complicated — and, in many ways, more interesting. Through leading the BBB AI Hub and training thousands of business owners, nonprofit leaders and workers on the responsible use of artificial intelligence, I have come to believe that AI literacy is increasingly becoming the difference between leverage and liability. I have watched people use AI to organize complex information, improve communication, accelerate research, analyze data and turn ideas into action faster than ever before. I have also watched people enter sensitive customer information into public AI systems, accept fabricated citations as fact, rely on biased or incomplete outputs and purchase products labeled “AI-powered” without really understanding what the technology does. The opportunity is enormous. But so is the responsibility. And nowhere is that becoming more evident than in the workforce.
PwC’s 2026 Global AI Jobs Barometer goes beyond the question of whether AI will “take jobs.” It explores how AI is changing the structure of work itself and what those changes could mean for employers, employees and people just entering the workforce.
Someone on your team has probably figured out a good way to use AI. Maybe it helps compare requirements against a deliverable, organize closeout documents, prepare an action log, or summarize project reporting. The prompt works. The result is useful. The person who built it knows exactly what to provide, how to phrase the instructions, and what needs to be checked afterward. Then someone else tries to use it. That is usually when the team discovers that a good prompt is not yet a repeatable construction workflow. The second person uploads the wrong version of the specifications. The output comes back in a different format. A requirement is missed. Nobody knows whether the AI used the contract, the addendum, or the folder labeled: FINAL — USE THIS ONE The problem is not necessarily the AI. The process was never fully defined.
Jonathan Liebert CEO of the BBB of Southern Colorado testifies before congress on the Impact of AI on the Workforce
AI already knows a lot. That is not really the problem. The challenge is teaching it what matters to your business. It does not automatically know your customers. It does not know your tone. It does not know your standards. It does not know your process. It does not know which details are important and which ones will send your team into a group text spiral. That is where the skill comes in. Using AI well is not just about asking better questions. It is about teaching AI what you want it to know so it can give you answers that are actually useful. That is the focus of the class I am teaching through the BBB AI Hub: Teaching AI your Business: Building Context That Actually Works. We are going to talk about how to use ChatGPT settings, Projects, and Custom GPTs to give AI better business context. Not in a complicated way. In a practical way. Because most business owners do not need more AI hype. They need to understand how to make AI useful in the work they are already doing.
Most industrial organizations are not starting from zero. They are starting from systems that already work—just not in ways that scale easily. Legacy workflows often represent years of optimization under real constraints. They are imperfect, but they are functional. This is why modernization is rarely a technical problem alone—it is a continuity problem. The goal is not to replace what works. The goal is to reduce friction while preserving operational stability.
Most industrial organizations are not starting from zero. They are starting from systems that already work—just not in ways that scale easily. Legacy workflows often represent years of optimization under real constraints. They are imperfect, but they are functional. This is why modernization is rarely a technical problem alone—it is a continuity problem. The goal is not to replace what works. The goal is to reduce friction while preserving operational stability.
Anthropic’s new Claude Fable 5 may be one of the most important AI model releases of the year, not just because of what it can do, but because of what it signals about where artificial intelligence is heading. Fable 5 is part of Anthropic’s new Mythos-class family of models, which sits above its previous Claude Opus models in capability. In plain English, this is not just a better AI model. This is a much more powerful system designed for complex work: software engineering, advanced research, long document analysis, scientific reasoning, visual understanding, and agentic workflows that can run across multiple steps. For business owners, that should get your attention. The promise of Fable 5 is significant. It can work on longer, harder, more complex assignments with greater endurance than earlier models. That means it may be useful for drafting strategic plans, reviewing large policy documents, analyzing contracts, supporting technical projects, building internal tools, developing training materials, summarizing research, or helping a team reason through complicated business decisions. This is the kind of AI that starts to feel less like an assistant and more like a high-level analyst. For small businesses, that is exciting. Many owners do not have a research department, a software team, or a strategic planning office. A model like Fable 5 could help smaller organizations access capabilities that used to be available only to large companies with deep benches of talent. That is the optimistic view. But there is another side to this release that small business owners should pay attention to: cost and access . Fable 5 is currently available in Claude for a limited time, with access expected to end on June 22, giving users a short window to experience one of Anthropic’s most advanced public models before access changes. But long term, this type of intelligence will not be cheap. Anthropic’s listed pricing for Fable 5 is reportedly $10 per million input tokens and $50 per million output tokens . For large enterprises, that may be manageable. For small businesses, nonprofits, and solo entrepreneurs, that pricing could quickly become a serious barrier. In everyday terms, this means Fable 5 may be affordable for an occasional high-value project, but expensive for constant daily use. Asking it to review one large report may cost only a few dollars. But using it all day across employees, documents, drafts, agents, and revisions could become a meaningful monthly expense very quickly. The concern is that Fable 5 is roughly double the cost of Claude Opus 4.8. That tells us something important: as models become more powerful, the best AI may not simply become cheaper. The market may split between everyday models for routine tasks and premium frontier models reserved for complex, high-value work. That may be the bigger story. For the past few years, small businesses have benefited from an incredible moment in technology: advanced AI was suddenly available through relatively affordable monthly subscriptions. A local business owner could access tools that felt almost magical for the price of a software subscription. That created a sense that increasingly powerful AI would always become cheaper and more accessible. Fable 5 raises a different possibility. What if the most powerful models become premium tools that only larger organizations can afford to use regularly? What if small businesses get access to “good enough” AI, while major corporations use the most advanced models for strategy, automation, coding, research, product development, and decision-making? That would be a major shift. Instead of AI closing the gap between small and large businesses, it could begin widening it again. Larger companies would have access to better reasoning, better automation, better agents, and better strategic support. Smaller firms might be forced to ration use, rely on cheaper models, or avoid advanced workflows because the cost is too unpredictable. This does not mean small businesses should ignore Fable 5. Quite the opposite. If you have access to it, test it. Use it on high-value work where quality really matters. Ask it to analyze a strategic plan, compare vendors, review a complicated proposal, summarize a large report, or help think through a major decision. Do not waste a model like this on routine emails or simple brainstorming. Use your most powerful AI where the return justifies the cost. There are also safety considerations. Anthropic has emphasized that Fable 5 includes safeguards around high-risk topics such as cybersecurity, biology, and chemistry. That matters because as AI becomes more capable, the risk of misuse also grows. Businesses should appreciate that the industry is taking safety seriously. But safety systems can also affect usefulness, especially when models become more cautious or restricted in certain areas. So where does this leave small businesses? The best approach is strategic adoption. Start by understanding which AI tasks are truly valuable for your organization. Use lower-cost models for routine work. Reserve frontier models like Fable 5 for complex, high-value projects. Train your team to understand when powerful AI is needed and when it is not. Track cost. Protect sensitive data. Keep humans involved in final decisions. Claude Fable 5 gives us a glimpse of the next era of AI: more powerful, more autonomous, more capable — and likely more expensive. That does not mean small businesses are out of the race. But it does mean they need to become smarter buyers and better managers of AI. The future of AI may not simply be about who has access to the best model. It may be about who knows when to use it, how to use it, and whether the value justifies the cost. That is the real AI Advantage. About the Author Jonathan Liebert is CEO/Executive Director of the Better Business Bureau of Southern Colorado, an AI thought leader and an adjunct professor at the University of Colorado Colorado Springs. He is the author of Thought Partner , which explores how leaders can collaborate with AI to improve decision-making and strategy. Jonathan also leads AI education and training programs through BBB of Southern Colorado to help businesses build practical AI skills for the modern marketplace.
