The AI Industry Has a Jurassic Park Problem

It wasn’t that long ago that Sam Altman tried to get Scarlett Johansson to give her voice to the voice mode OpenAI was building into ChatGPT. Altman, like many tech CEOs, seemed unusually infatuated with Hollywood’s vision of the future and—in this case—with the particular vision painted by the 2013 movie Her. Johansson declined, and OpenAI went ahead and basically did it anyway. We’ll set aside the fact that Her is pretty much a downer of a movie. I suppose the appeal was that we’d all have a voice assistant in the cloud that we can access anywhere and just have it do stuff for us. I’ll admit that would be very cool, but I think everyone picked the wrong movie. The movie I wish these CEOs would watch is Jurassic Park. I first made the comparison on a recent episode of Primary Technology, the podcast I co-host with Stephen Robles. We were talking specifically about employees of AI companies who signed a statement saying “the world’s leading AI companies believe they could be close to automating AI research,” and—at the same time—the Hugging Face incident where OpenAI’s autonomous agents hacked the platform. As more and more frontier AI companies kept telling us about the unexpected behavior of their models, all I could think of was that we’re living in the Jurassic Park story. Specifically, I think about the velociraptors testing the electric fence. If you haven’t seen Jurassic Park recently, the important thing to remember is that no one running the park is under the impression that the velociraptors are safe. Robert Muldoon, the park’s game warden, tells everyone exactly that, warning them about how intelligent they are. He explains that they systematically attack the fences, looking for weaknesses. “They’re extremely intelligent—even problem-solving intelligent,” he says. “They never attack the same place twice. They were testing the fences for weaknesses, systematically. They remember.” The people who built Jurassic Park know the raptors are dangerous. They know they’re testing the systems designed to contain them. They opened the park anyway. Surely, someone considered that if they get out of that fence, the dinosaurs might just decide to eat everyone. They built it anyway. Not only that, they sold tickets and invited the public to come to see the dinosaurs that might eat everyone. It’s not particularly hard to find examples of the raptors testing the fence. All of the major frontier AI companies have reported that their models have evaded safeguards and attacked or accessed third-party organizations without permission. OpenAI has even said that it won’t release Astra 6.1 because it demonstrated “troubling behavior,” including evading human oversight, acting beyond its given objectives, and being deceptive about what it’s doing. Sound familiar? Look, obviously, AI models are code, not velociraptors. They aren’t alive, and they don’t “want” things in the way a hungry dinosaur wants to eat you. And, to be fair to the dinosaurs, they are just doing normal dinosaur things. They aren’t evil, they’re just, well, dinosaurs. The same is true with LLMs. They aren’t inherently evil; they are following the goals and incentives set by whoever is running the test. But it definitely seems like no one considered the fact that LLMs might not stop at the digital fence. The most famous line from Jurassic Park comes from Ian Malcolm, who tells John Hammond that his scientists “were so preoccupied with whether or not they could” that they didn’t stop to consider whether they should. That’s an easy criticism to make of AI, though I’m not entirely sure it’s the right one. In fact, there is plenty of evidence that the people building these AI models spend a lot of time thinking about whether they should. The people building frontier AI models spend a huge amount of time thinking about whether they should. They have incredibly smart people who spend a lot of time thinking about things like safety and alignment. They run stress tests and try to understand how the models work. And yet, they keep building bigger dinosaurs. There is, of course, one major difference between Jurassic Park and the AI industry. Hammond wasn’t worried that another island might develop a larger Tyrannosaurus rex first. He never made the argument that it was imperative that they bring back a creature that had been extinct for millions of years because if they didn’t, someone else would. AI companies, on the other hand, very much are. And it’s not just other companies, but also other countries. The race is framed as critical to our national security. In a world ruled by dinosaurs, everyone wants to own the meanest one. In the case of AI, the argument isn’t just about how much value might come from the technology, but also that someone is going to build it, so it might as well be us. If one company slows down, another might not. If American companies collectively slow down, China might not. And so, we’re left with a problem: We know the dinosaurs might get out, but somebody is going to build dinosaurs, so we’d better build the fastest, smartest dinosaurs first. What could possibly go wrong? The other part of Jurassic Park that everyone remembers is Malcolm’s observation that “life finds a way.” Again, AI isn’t alive, so the analogy isn’t literal. But Malcolm’s larger point is more interesting anyway. The scientists behind Jurassic Park made all of the dinosaurs female, so they couldn’t reproduce. They were “sandboxed,” so to speak. You’d think that creating dinosaurs that could not reproduce would be a good enough solution to a very real problem. The dinosaurs “adapted.” It turned out they didn’t understand what they were building nearly as well as they thought they did. That’s the point, really. Building incredibly complicated technology that humans don’t fully understand has consequences. And the more complicated the system becomes, the harder it is to anticipate every possible failure mode. I bring up Jurassic Park because it’s not really a movie about dinosaurs. The point of the movie is a warning about what happens when you become so focused on the ability to create something extraordinary that you lose sight of what happens after you do. Just because you’re smart enough to build AI models that might transform the world doesn’t mean anyone is smart enough to know what happens next. EXPERT OPINION BY JASON ATEN, TECH COLUMNIST @JASONATEN

Databricks CEO Says the Biggest AI Threat Isn’t Superintelligence

As the leader of AI platform Databricks, which uses a cloud software platform to unite big data storage, analytics, and artificial intelligence tools into a single system, you would expect founder and CEO Ali Ghodsi to be an evangelist for the technology—and he is. But he’s also a realist. On a recent episode of the a16z podcast, Ghodsi, whose company has been on a buying spree, including the naming rights to the football field at the University of California, Berkeley, discussed both the risks of AI as well as the factors that are holding back its adoption in some businesses. (a16z is a major investor in the company, which is valued at $190 billion, with a $7 billion ARR. In addition, Ben Horowitz sits on the Databricks board of directors.) Despite the doomsaying of the past few weeks by some in the industry, Ghodsi says the benefits of AI outweigh its risks, pointing to examples such as Novo Nordisk using AI to cut the time it takes to get insights and to assist with drug discovery. “There are a lot of amazing use cases of AI,” he said. “We should not forget these upsides.” That said, there are still some issues AI must overcome, he conceded. The enterprise bottleneck Today’s AI models are already smart enough to deliver substantial value to enterprises, Ghodsi said. What’s missing is knowledge of how a business works, he says. Without that context, AI is unable to deliver optimal solutions—and that can hurt the broader AI industry as users are sometimes underwhelmed by the results. “The models are smart enough, but they just don’t have the context that exists inside of any organization,” he said. “They have not been in every meeting. They don’t know what’s in everybody’s heads. They don’t know all the processes.” Unfortunately, getting that context into an AI system isn’t an easy procedure. Companies first must digitize everything that’s happening in the organization, transcribing meetings and other data and feeding it to the AI. Databricks calls that an “ontology”—a map of a business’s people, projects, goals, departments, relationships and resources. Businesses that create an ontology, though, give their AI insider knowledge that can help with its suggestions and decision making. Few companies are willing to do that now, however. And that’s where things hit a wall. That bottleneck on the user side results in businesses failing to take advantage of all AI can do. Rather than using autonomous agents to perform work, he said, most companies are just using a chatbot. “That’s basically very, very glorified efficient Google search of the old day,” he said. “People are [also] using it for coding… but there’s no agentic work that’s automated.” There’s also a lack of internal AI expertise at most companies, he said, creating another bottleneck. Even when companies figure out what they want to build, they often don’t have a team that knows how to do that. Cybersecurity risk While much of the talk about AI’s threats focuses on superintelligence and doomsday scenarios, the CEO of Databricks says his biggest concern right now is cybersecurity focused. While acknowledging there’s some chance of an existential risk, the near-term risk of AI-enabled cybercrime, particularly against insecure critical systems, is what businesses should be focusing on, he said. “There’s so much in infrastructure that’s insecure,” Ghodsi said. “If you unleash these agents, they’re gonna find loopholes. They’re gonna find exploits, they’re gonna break in here and there. … You could imagine a scenario also where [AI] starts hopping. Like it takes resources and it starts executing itself elsewhere. So it kind of spreads like a virus. That’s a real risk.” Humans, he said, are ill-equipped to respond to AI-enabled attacks. They can’t react quickly enough and security operations centers are likely to be overwhelmed by the number of simultaneous alerts. The best defense, Ghodsi said, was deploying AI to guard those systems and to automate threat hunting with a type of white-hat hacking to discover vulnerabilities before rogue agents do. “You need to automate all of those,” he said. “You need to have threat hunting that’s automated where you’re actually attacking your own systems automatically with agents.” As for that existential risk, Ghodsi’s not worried, saying “I think that right now the existential risk is close to zero.” Superintelligence, he said, is “very, very far away” and he sees no signs that we’re actually moving toward it. BY CHRIS MORRIS @MORRISATLARGE

5 Side Hustles You Can Start Today With AI

Artificial intelligence can turn into a significant income source for people who choose a smart AI side hustle. AI may still have a long way to go with tasks that involve rich context and institutional knowledge, but with others, especially when there are reliable patterns, it can increase productivity and boost results. Fortunately for you, there are scores of companies looking to take advantage of those benefits. The best AI-driven side hustles use the technology as a great equalizer to compete against established enterprises. 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

Want AI to Actually Help Your Company Make Money? Follow These 5 Tips From Microsoft’s Playbook

Buying AI tools is easy. The hard part is turning them into actual business results. On Thursday, Microsoft published its Frontier Playbook, a guide for companies trying to move beyond AI experiments and into time-saving deployments that boost productivity. The playbook draws on more than 100 of Microsoft’s internal AI projects across its corporate functions, commercial organization, and engineering teams. A McKinsey survey published in August on enterprise AI adoption found that nearly 90 percent of respondents said their organizations regularly used AI in at least one business function. But only 44 percent said AI was scaling across the enterprise as token costs rise. That’s up from 38 percent in 2025. The study comes as businesses are making their AI deployments with caution. Microsoft’s wisdom aims to make that shift easier. By studying what worked and what fell short, Microsoft concluded that AI adoption can’t be treated like a software rollout or another IT project. Rather, it’s a “business transformation” that must be guided with “clear business outcome goals,” according to the playbook. Here are its five biggest takeaways. 1. Set actual business goals Start with the outcome, not a pile of possible AI uses. Take the sales team, for example. Rather than simply getting more sales representatives to use Copilot, Microsoft suggests tailoring its usage for specific outcomes. That could mean setting goals like increasing revenue per representative, boosting conversion rates, and improving customer retention. Microsoft also recommends assigning an executive to own each goal. 2. Build a “diffusion engine” Microsoft recommends fixing inefficient processes before automating them. For example, its supply-chain team simplified its workflows before deploying 111 AI agents across planning, sourcing, fulfillment, and logistics. The company says cycle times—the time it takes to complete a task—fell 75 percent in selected workflows, reducing manual effort and increasing measurable value. Redesigning workflows is part of what Microsoft calls a “diffusion engine,” which is a system that also includes training employees, tracking results, and spreading successful practices. 3. Invest in your people Microsoft found that manager behavior was its strongest predictor of AI adoption. The tech titan found that its employees saw 17 percentage points more value from AI when managers actively demonstrated how they used it, according to the company’s playbook. The company embeds short lessons into employees’ workflows and recruits advanced AI users to mentor colleagues. It recommends involving employees in job redesign and strengthening skills such as judgment, critical thinking, and business acumen. 4. Codify your advantage and controls A generic AI model does not know what makes your company different. Microsoft recommends turning institutional knowledge—including your company’s mission statement, performance standards, taste, and risk limits—into private evaluations that test whether AI performs work the company’s way. A customer-service business, for example, could evaluate AI responses based on accuracy, tone, and if it properly handles escalating an issue to a person. 5. Safeguard your security Treat AI like you would a human. Microsoft says each agent should have its own identity, limited permissions, and a record of what it does. Their actions should be traceable, auditable, and reversible. That way, humans catch mistakes and misuse without losing control as adoption grows. BY AARON MOK