This AI Godfather Says Business Tools Built on LLMs Are Doomed

Silicon Valley firms and countless other businesses across the country are spending billions of dollars to develop and adopt artificial intelligence platforms to automate myriad workplace tasks. But top global technologist Yann LeCun warns that the limited capabilities of the large language models (LLM) those apps and chatbots operate on are already well-known, and will eventually be overmatched by the expectations and demands users place on the systems. And when that happens, LeCun says, even more investment will be required to create the superintelligence technology that will replace LLM-based AI—systems he says should already be the focus of development efforts and funding. While that may seem like an outlier view, LeCun, 65, is far from a tech outsider. The Turing Award winner ran Meta’s AI research unit for a decade, only leaving last November to launch his own Paris-based startup, Advanced Machine Intelligence Labs. In addition to disliking the managerial duties that came with the research-rooted Meta job, LeCun said his departure was motivated by his view that Silicon Valley has prioritized short-term business interests over far more important and attainable scientific objectives. Top of those commercial concerns he cites was developing and marketing LLM-based AI chatbots and apps with limited capabilities, rather than superintelligence systems with virtually boundless potential. In contrast to current AI, which uses collected data to provide responses to questions or perform necessary tasks, superintelligence systems take in all kinds of surrounding information they encounter, including text, sound, and visual input. They use all of this not only to teach themselves how to respond to data feeds effectively, but also to predict what’s coming next—a requisite for truly self-driving cars, say, or robots that reason and react as humans would. The vast differences in what current LLM-based AI and emerging superintelligence systems can offer mean that countless businesses are now buying and adapting a technology LeCun predicts is destined to be replaced by something better. And not because it’s more effective—and certainly not less expensive—but because that’s how the tech sector decided the fastest profits were to be made. Human-level intelligence “There is this herd effect where everyone in Silicon Valley has to work on the same thing,” LeCun told the New York Times recently. “The entire industry has been LLM-pilled… [but] LLMs are not a path to superintelligence or even human-level intelligence.” To be sure, AI apps like OpenAI’s ChatGPT, Microsoft’s Copilot, and Anthropic’s Claude have continually been improving over time, as they automate workplace tasks like emailing, content composition, and research. But LeCun says the fact that their LLM models rely on gathering, digesting, and working from word-based data limits how far they can evolve to approach—much less surpass—human thinking and response capabilities. By contrast, he and fellow researchers at AMI Labs are creating “world models” also trained with sound, video, and spatial data. Over time, they are expected to be able to observe, respond to, and even predict user activity and physical environments in countless workplace settings. And that’s expected to allow them to collect both more and broader ranges of information than humans can and react in ways people would if they had those capabilities. “We are going to have AI systems that have humanlike and human-level intelligence, but they’re not going to be built on LLMs,” LeCun told MIT Technology Review this month, describing the models AMI Labs and other researchers are working on. “It learns the underlying rules of the world from observation, like a baby learning about gravity. This is the foundation for common sense, and it’s the key to building truly intelligent systems that can reason and plan in the real world.” But what does that mean for business owners—not to mention investors—spending huge sums to develop, acquire, and use LLM-based AI apps? If LeCun is correct, all those tools being marketed as the future of workplace productivity will become obsolete in several years and be replaced by the superintelligence tech he believes should have been prioritized in the first place. There’s already some evidence backing LeCun’s view that Silicon Valley has focused on the shorter-term profit objectives of rushing capacity-limited LLM apps to market, despite being aware of the limitations of their effectiveness. For example, a study published last August by MIT Media Lab’s Project Nanda estimated that despite the $30 billion to $40 billion that’s been invested since 2023 to develop or purchase AI platforms, only 5 percent of businesses that bought those automating tools have reported any return on that spending. “The vast majority remain stuck with no measurable [profit or loss] impact,” it said. And despite increasing investment in AI tech by businesses—and swiftly rising use by workers—there’s considerable disagreement on how effective the platforms actually are. According to a Wall Street Journal study, 40 percent of C-suite managers credited the work-automating apps with saving them considerable time each week. By contrast, two thirds of lower-level workers said the tech saved them little or no time at all. LeCun doesn’t appear to regard any ROI or performance questions during this still-early era of AI tech as the problem. He even thinks LLM-based apps are valuable—up to a point. For example, he compliments most apps and chatbots Silicon Valley has developed and sold to businesses as being very useful to “write text, do research, or write code.” AI’s unscalable apps But LeCun says the enormous fortunes and business strategy commitments Silicon Valley has made in what he views as a relatively short-term technological solution ignore the bigger, long-term potential of automating technology’s next phase. Meaning, in cumulative terms, it will make the broader effort to produce and perfect AI more expensive. In his view, much of the money and froth that’s inflated what critics call today’s AI bubble will likely vanish when the models of today’s apps and chatbots can’t be used to build tomorrow’s revolutionary tech. “LLMs manipulate language really well,” LeCun told MIT Technology Review. “But people have had this illusion, or delusion, that it is a matter of time until we can scale them up to having human-level intelligence, and that is simply false.” Ironically, even LLM-based apps using available data concur that superintelligence systems will offer huge advantages when (not if) they supplant today’s AI tools. “While LLMs are incredibly powerful tools for generating text and interacting with humans, a true superintelligence would represent a leap beyond these current systems in terms of understanding, autonomy, adaptability, and practical real-world impact,” ChatGPT replied when asked about its eventual replacement—providing eight major improvements superintelligence tech will offer. When those systems do come online, LeCun says, businesses recognizing their far wider range of applications will have no choice but to buy them to replace outdated LLM-based AI tools they’ve just recently acquired. “Think about complex industrial processes where you have thousands of sensors, like in a jet engine, a steel mill, or a chemical factory,” LeCun told MIT Technology Review. “There is no technique right now to build a complete, holistic model of these systems. A world model could learn this from the sensor data and predict how the system will behave. Or think of smart glasses that can watch what you’re doing, identify your actions, and then predict what you’re going to do next to assist you. This is what will finally make agentic systems reliable.” And superintelligent systems hopefully won’t generate photos of people with six fingers or endless volumes of workplace slop for employees to plow through. BY BRUCE CRUMLEY @BRUCEC_INC

Early-Stage AI Companies to Watch in 2026

Artificial intelligence is entering its fourth year as the most talked-about force in business. Since ChatGPT’s launch in 2022, AI has upended and reshaped workflows in countless industries, and continues to dominate boardroom conversations and investor strategies. This year, a new wave of early-stage startups is emerging with bold ideas and transformative technologies. They aren’t looking to replicate the world-altering success of OpenAI, but rather to leverage technological advancements in AI to solve niche issues. For example, Tim Tully, an investor at venture capital giant Menlo Ventures, predicts that AI-powered sales and go-to-market tools will break out in 2026. Still, the difference between startups that succeed and those that fail will be a strong intuition for product management—and for founder tenacity. And speaking of founder tenacity, Kulveer Taggar’s venture fund, Phosphor Capital, invests exclusively in “top founders in each Y Combinator batch.” (His cousin, Harj Taggar, is a managing partner at YC.) A two-time alum of YC, Kulveer Taggar is looking for “customer-obsessed” founders and businesses that remind him of the startup accelerator’s most successful alumni, like Airbnb and Stripe, when they were just starting. Based on their suggestions and Inc.’s research, here are some early-stage AI companies poised for game-changing success. 1. OpenEvidence Founder: Daniel Nadler Location: Miami Founded in 2022 by Canadian entrepreneur Daniel Nadler, OpenEvidence produces a medical AI assistant often dubbed “ChatGPT for doctors.” The company’s platform uses large language models specifically trained on massive amounts of clinical data, medical research, and electronic health records to provide real-time recommendations, diagnostic support, and administrative assistance to health care professionals. Since its founding, OpenEvidence has secured major partnerships with several large hospital systems across the United States and Europe, allowing it to rapidly test and refine its models in clinical settings. The company says that its medical search engine is used on a daily basis by more than 40 percent of physicians in the U.S. today. In January 2026, OpenEvidence announced that it had raised a $250 million Series D round, at a valuation of $12 billion. OpenEvidence wrote in a statement that the new funding will be used “to invest heavily in the R&D and compute costs associated with the multi-AI agentic architecture of OpenEvidence, which provides the highest quality and most accurate medical answers of any system in the world.” Over the past 12 months, OpenEvidence has raised a grand total of $700 million. 2. AMI Labs Founder: Yann LeCun Location: Paris Yann LeCun, the acclaimed NYU professor, 2018 Turing Award winner, and former chief AI scientist at Meta, has launched his first startup, making him one of the most-watched figures in AI. Announcing his December 31 departure from Meta via LinkedIn, LeCun revealed plans for a new company dedicated to his research into advanced machine intelligence (AMI). The company’s goal, he wrote, is to drive the “next big revolution in AI: systems that understand the physical world, have persistent memory, can reason, and can plan complex action sequences.” These systems are also called “world models,” and they will be where LeCun focuses his attention. FIVE AI TRENDS WILL SHAPE IT IN 2026: Our report unpacks the macro trends shaping AI and ties each one back to IT strategy, governance, and transformation. 1. Foundational AI principles will rewrite organizational DNA Enterprises will develop their own guiding AI principles to address rising AI risk and align their AI strategy around core organizational values. 2. From copilots to vibe coding: AI will continue to reinvent IT New categories of enterprise AI tools will emerge, propelling many organizations toward a crucial decision: AI platform or best-of-breed AI tools? 3. Agentic AI will come of age and power the exponential enterprise Although current adoption of agentic AI is low, it will grow faster than generative AI, powering exponential growth and change across organizations while bringing new opportunities and risks. 4. Risk management will be the price of admission for AI The potential risks of new AI applications will drive organizations to adopt AI risk management programs, even in jurisdictions with no regulatory requirement. 5. AI will hang in the balance between freedom and control AI sovereignty will become top of mind for regulators, but legislative policies will develop in a disjointed fashion around the world.

6 Consulting Trends to Watch in 2026

The year 2026 promises to be an exciting one for the consulting industry. Big technological changes and a growing number of startups in the space are expected to accompany a period of client recalibration. To succeed, consulting firms will need to be adaptable—and will need to stay on top of industry trends. Flexibility in how you operate and how you approach clients can open new sources of revenue. And by boosting your tech savviness, you can offer better solutions, which could increase repeat and referral business. Here’s a look at some of the most notable consulting trends that will impact the industry in the year to come. 1. Niche specialists will be in greater demand than generalists There’s a growing shift away from generalist consulting firms and a greater demand among clients for specialists, who focus on specific areas of expertise. The growing number of independent consultants is filling that demand, with detailed sector knowledge and a firm understanding of that area’s regulatory frameworks, ESG compliance, or sector-specific nuances. Those companies typically offer faster turnaround times on projects, better insights, and improved impacts for clients. “Clients prefer boutique firms offering regulatory expertise, sector specialization, and competitive pricing, driving growth in niche consulting segments,” says research firm StartUs Insights. 2. Expect more competition, but new avenues of revenue It’s not the news that some firms want to hear, but the hard truth is 2026 will see a much more crowded consulting landscape. Professionals who were let go in 2025 (and will be this year) are increasingly turning to consulting as the job market becomes leaner, often giving well-established companies more competition than they were counting on. At the same time, client budgets are likely to be leaner, which could impact the number of jobs they commission. That doesn’t mean the work won’t be there. Greaux Consulting says flexibility will be key, as clients could look for fractional or on-demand consulting help. And while big corporations could reduce budgets, there’s likely to be more demand from small and midsize businesses for consulting expertise. Expect “a growing demand for business consulting services for small businesses, as this provides small emerging companies access to skilled, high-level experts to consult on operations, financial planning and projections, and marketing strategy,” says the company, which specializes in business process documentation, operational transformation, and executive coaching. 3. Localization will become much more important With the emphasis from the White House and Congress on domestic workers, many companies are foregoing working with international consultants or opting to work with those who have strong local networks. That could present an opportunity. Additionally, while there’s a lot of volatility in the economy now, some areas, especially in the Southeast, are seeing expansion, which will create more demand for consultants who know those regions, their regulations, market conditions, and workforce dynamics. Companies that are looking to expand to those areas will need expert insight into how best to grow and succeed there. 4. AI expertise will be in demand It likely won’t come as a surprise to hear AI will play a bigger role in client operations in 2026. As companies depend on it to assist with everything from analytics to forecasting, consultants who are fluent in these tools will have a competitive advantage. And those who can offer the tools to clients could have an even bigger head start. “Organizations that hire consulting services or a national consulting practice will see meaningful benefits from AI-driven tools to improve operations, customer segmentation, supply chain, and financial planning,” says Greaux. 5. There will be an increased focus on automation With businesses becoming even more focused on optimization in 2026 (a trend that has been growing for the past several years), consultants who can help guide them through reworking manual processes into automated ones could be in a position to thrive. There’s a growing need to reduce costs, increase productivity, and enhance efficiency, which opens up a niche for consultants. StartUs Insights says “the digital transformation consulting market [will grow] from $268.46 billion in 2025 to $510.50 billion in 2034.” 6. Personalizing client services will boost revenues While a one-size-fits-all approach might have worked for consulting firms in years past, there’s a growing movement toward selecting firms that offer a methodology that’s tailored specifically to the client or its industry, addressing their unique issues with adaptive, data-driven solutions. McKinsey says consulting firms that focus on personalization typically see revenues that are 10 to 15 percent higher – and, in some cases, up to 25 percent. They also have a higher client retention level. BY CHRIS MORRIS @MORRISATLARGE

Apple’s Rumored AI Pin Forces a Simple Question: What Do People Actually Want?

Earlier this week, my co-host and I had a conversation on our podcast, Primary Technology, about the rumors that Apple is working on an AI pin. We don’t usually spend a lot of time on rumors—for a number of reasons I won’t get into here—but this one is particularly interesting considering that we’ve seen this before (AI pins, I mean), and they haven’t turned out especially well. According to a report from The Information this week, Apple is actively developing a wearable device—roughly the size of an AirTag—equipped with cameras and microphones but notably, lacking a display. The idea is that it would launch as early as 2027, powered by the kind of multimodal intelligence we expect to see in iOS 27. Since we recorded that episode, I’ve been thinking a lot about whether this makes any sense for Apple, and what exactly the ideal device is for AI. I’ve said in the past that I think that’s the Apple Watch, though a “pin” definitely has certain advantages (it has an outward-facing camera, for example). More importantly, I’ve been thinking about what the ideal AI device is based on what people actually want. The ideal form factor should be determined by the ideal use cases, not the other way around. I think the bar is pretty high. If I’m going to carry another device, or if I’m going to replace something like my iPhone, it has to be able to offer value I can’t get from what I already have. Generally, that falls into three buckets: Answers to questions The most obvious use case is what people already use AI tools for—getting information. Of course, this is admittedly a pretty broad category. There are a lot of different types of information that people might want. For example, they might want to ask simple questions like “who directed Star Wars: The Last Jedi?” But people also want to ask slightly more complicated questions (what’s the weather going to be like when my plane lands tomorrow?), as well as queries like “what’s this plant, and is it edible?” Those questions require a different kind of contextual awareness. Your AI assistant has to be able to see your calendar, find out flight information, and check the weather at your destination. Or, in the latter case, it has to be able to literally see what you’re talking about, identify the plant, and give you the information you’re looking for. This is where the form factor of a pin actually starts to make some sense. The primary limitation of Siri on your iPhone or Apple Watch is that it can’t see what you see. Sure, you can hold up your phone and point the camera at stuff, but that’s awkward. If Apple’s rumored device includes the dual-camera array mentioned in the reports, it changes Siri from being just a voice assistant to a multimodal source of information about the world. You aren’t just asking for information; you are asking for context about the physical world in front of you. Do things on their behalf Of course, getting information is great, but acting on it is even more useful. This is the “agent” concept we’ve heard so much about but haven’t really seen work in practice. It’s the promise that the Rabbit R1 made but couldn’t keep: the ability to interface with apps and services to actually get things done. The Rabbit R1 failed because it tried to simulate your interactions via a cloud-based “Large Action Model” that was clunky and unreliable. Apple has the potential to solve this for first-party apps like Calendar and Messages. It controls the entire software stack, meaning it can offer an experience that other devices couldn’t. And, with App Intents, Apple could solve the same problem for other apps if it could get third-party developers on board. I don’t just want to know that my flight is delayed; I want the device to rebook me on the next one and update my calendar. We’re a long way off from any device being able to do that, but it’s the promise that every company keeps making. If Apple can make it happen, it’ll immediately jump to the lead. Remember and prompt This is the “external brain” use case, and frankly, it’s the one a lot of people find most compelling. We all have those moments where we meet someone and can’t quite place them, or we have a brilliant idea while driving and lose it by the time we get home. An ideal AI device should be a passive observer that helps you connect the dots. It should be able to whisper in your ear, “That’s David; you met him at CES last year,” or remind you to pick up milk because it knows you’re near the grocery store. Of course, this is also the creepiest use case. It requires a level of always-on surveillance that most people are rightfully uncomfortable with. If Apple is going to ask us to wear a camera and microphone on our chests, they are going to have to lean incredibly hard on their privacy credentials. Trust is the only currency that matters here. The big risk Previous devices haven’t been much of a success. No one has figured this out yet. The Humane AI Pin was a disaster of overheating and poor battery life. The Rabbit R1 was barely functional. The history of wearable AI is short, but it is brutal. There are laws of physics that even Apple cannot ignore. Cameras and AI models generate heat and drain power. Putting that in a coin-sized aluminum disc without a massive battery pack is an engineering feat no one has cracked. There’s also the fact that wearable devices come with a very real stigma. Anything that isn’t a watch has to be exponentially more useful than the burden of wearing it. Google Glass failed partly because people simply didn’t want to talk to someone who had a camera pointed at their face. Meta has circumvented this slightly with Ray-Bans because they look like sunglasses. A shiny badge on your chest is a much bolder statement. Is that an argument for or against Apple trying? I’m not sure. But with reports that Jony Ive and OpenAI are building their own hardware, Apple may feel it cannot afford to cede the category. Even if, right now, it looks like a solution in search of a problem. EXPERT OPINION BY JASON ATEN, TECH COLUMNIST @JASONATEN