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What began as a race to build better AI models has escalated into a competition for compute, talent, and control. Foundation modelslarge-scale systems trained on vast datasets to generate text, images, code, and decisionsnow underpin everything from enterprise software and cloud infrastructure to national digital strategies. The industrys language around AI has grown more ambitiousand more elastic. Agentic AI has leapt from research papers to Davos billboards, while artificial general intelligence, or AGI, now appears routinely in investor decks and earnings calls. Definitions have begun to blur. Some companies quietly lower the bar for what qualifies as general, stretching the term to encompass incremental productivity gains. Yet the economic results, particularly measurable returns on AI investment, remain uneven. According to PwCs 2026 Global CEO Survey, 56% of 4,454 CEOs across 95 countries reported neither increased revenue nor reduced costs from AI over the past 12 months. Only 12% achieved both. Even so, 51% plan to continue investing, despite declining confidence in revenue growth. The result is a widening gap between engineering reality, commercial storytelling, and public expectation. Few voices carry as much authorityor have shaped modern AI as directlyas Andrew Ng. The founder of DeepLearning.AI and Coursera, executive chairman of Landing AI, and founding lead of the Google Brain team, Ng has helped define nearly every major phase of the field, from early deep-learning breakthroughs to the current wave of enterprise deployment. He has authored or coauthored more than 200 papers and previously led the Stanford AI Lab. In 2024, he popularized the term agentic AI, arguing that multistep, tool-using systems capable of executing workflows may deliver more near-term economic value than simply scaling larger models. In an exclusive conversation, Ng offered Fast Company a reality check. He says true AGIthat is, AI capable of performing the full breadth of human intellectual tasksremains decades away. The true competitive frontier, meanwhile, lies elsewhere. This conversation has been edited for length and clarity. You helped popularize the term agentic AI to describe a spectrum of autonomy in AI systems. How did you come up with it, and how has the concept evolved as multi-agent systems move into enterprise production? I began using the term almost two and a half years ago, though I didnt publicly take credit for it at the time. I started using it because I felt the community needed language that shifted the focus toward AI systems capable of taking multiple steps of reasoning and actionnot just a single prompt-and-response exchange. More specifically, I felt there would be a spectrum of AI systemssome slightly autonomous or slightly agentic, and others highly agenticwhere they take many steps of actions and work for a long time. No one was using the term agentic to describe this concept before I began using it. I started introducing it in my newsletter and in talks at conferences and industry events, and it quickly gained traction there. I didnt expect marketers to run with it the way they did. When I attended Davos this year, I saw the word plastered on the sides of buildings. Even outside San Francisco, agentic now appears on billboards. I did want to intentionally promote the use of the term, but seeing how common it has become, I sometimes wonder if I overdid it. Enterprise adoption of agentic AI is accelerating, yet many organizations are struggling with integration, governance, and measurable ROI. Why is it so? Two years ago, there was intense hype around AIs risks and dangers, among other concerns. Last year, businesses began shifting their focus toward real-world implementation. This year, the conversation has moved firmly to ROI. Even though many companies are not yet seeing strong returns, they continue to invest because they understand that AI will eventually deliver value. The discussion has shifted from excitement about what AI might do to a more grounded focus on how it can generate real economic impact. Theres also an interesting split-screen dynamic emerging. On one hand, many businesses say agentic AI is not yet delivering meaningful ROI, and theyre right. At the same time, teams building agentic workflows are seeing rapid growth and real, valuable implementations. The agentic movement still has very low penetration, but it is compounding quickly. What are the most significant mistakes enterprises make when deploying agentic systems at scale, and how should leaders rethink their technology and operating models to overcome them? Many businesses are pursuing bottom-up innovation, which is valuable, but the limitation is that it often leads to point solutions that deliver incremental efficiency gains rather than transformative change. If AI automates just one step in a process, for example, it might save an hour of human work and reduce costs. Thats useful and worth doing, but it doesnt fundamentally change the business. Much of todays AI deployment falls into this categoryincremental improvement rather than full transformation. To unlock real value, companies need to look beyond optimizing individual tasks and start reimagining entire workflows. Doing so requires top-down leadership. Often no single person working on one step has the authority to reshape the entire process, which is why executive-level direction becomes essential. Real impact comes from tailoring AI strategy to each organizations specific context rather than following generic industry playbooks. There is a growing debate about whether we are in the midst of an AI bubble or simply an early infrastructure build-out comparable to the internet era. How do you distinguish between speculative hype and genuinely durable AI value being created today? At the application layer, I dont think were in an AI bubble. AI is expanding rapidly across business use caseshow we process legal and technical documents, manage customer success workflows, conduct research, and much more. I would like to see more investment in AI applications and inference infrastructure. Right now, there simply isnt enough inference capacity, and worries around rate limits exist. The more interesting question about a potential bubble sits in the model training layer, where infrastructure spending continues to surge. If any risk exists, its highest there because the largest investments are concentrated among a small number of players. When companies build highly specialized hardware that can only be reused for inference with some inefficiency, the risk of overbuilding increases. I dont think were overbuilding right now, but if any part of the AI market faces that possibility, its the training layer. As the industry moves beyond a single-model mindset toward more diverse agentic systems, how should enterprises think about AI architecture? Is there likely to be one dominant framework for building scalable, real-world AI systemsor will organizations need a more flexible approach? Software can range from five lines o code to massive systems that run for years. Because of that range, there wont be a one-size-fits-all approach to building or governing these systems. Just as we dont use a single framework to manage everything from simple scripts to enterprise platforms, we wont rely on one architecture for agentic AI. Human work itself is incredibly diversefrom basic tasks like spell-checking to analyzing complex financial documents. Since the work varies so much, the AI systems we build will also need to vary. One principle my teams follow when building agentic AI systems is speed, as continuous improvement is essential. Our typical cycle involves building carefully to avoid major risks, testing with users, gathering feedback, and refining the system until it truly works well. That rapid loop is what helps teams build reliable, high-performing systems faster. Agentic AI is rapidly increasing systems ability to reason and act with limited human intervention. Does the rise of agentic architectures meaningfully accelerate the path toward AGI, or are we still far from true general intelligence? Most of the public thinks of AGI as AI that is as intelligent as people, and one useful definition is AI that can perform any intellectual task a human can. You and I could learn to fly an airplane with maybe 20 hours of training, learn to drive a truck through a forest, or spend a few years writing a PhD thesis. Most humans can do these things. Were still very far from AI meeting that definition of AGI. For alternative definitions that some businesses have put forwarddefinitions that dramatically lower the baryou could argue we already achieved AGI. Theres a good chance that under these lower-bar definitions, some businesses will soon try to declare success. But that wont mean AI has reached human-level intelligenceit will simply mean the definition has been reworked to fit a much lower threshold. Maybe a year ago, AGI felt 50 years away. Over the past year, perhaps weve made a solid 2% of progress, with another 49 years to go. These numbers are metaphorical, so dont take them too seriously. [Laughs] But we are closer than before, yet many decades away from an AI that matches human intelligence. If you stick with the original definitionaligned with what people genuinely imagine AGI to bewe remain very, very far away. Is geopolitical fragmentation reshaping global AI strategy for both governments and enterprises? One of the other big themes Im seeing is sovereign AI. The world is becoming more fragmented, and theres a lot of discussion about how nation-states want to make sure they have access to AI without needing to rely on other nations or any single company that they may not fully trust or be able to rely on in the long term. Governments and regions are thinking carefully about how to build and maintain their own AI capabilities so they can remain competitive and secure. As AI becomes more central to economic growth and national security, this question of who controls the infrastructure and models becomes much more important. So alongside enterprise adoption, theres also a growing geopolitical dimension to AI deployment. In 2026, as enterprises search for real economic returns from AI, what leadership decisions and workforce shifts will ultimately determine whether organizations capture meaningful value from agentic systems? Leadership matters. When I work with CEOs, I see decisive moments when the C-suite must think strategically about what to invest in and then place those bets thoughtfully, guided by a clear understanding of what the technology can and cannot donot just the surrounding hype. In periods of transformation, leadership decisions determine whether an organization captures real value from AI or merely experiments at the margins. I often speak with CEOs before they set a major strategic direction. No one knows exactly where AI will be in a few years, so we are operating in a kind of fog of war. But uncertainty does not mean we dont know anything. Teams and partners who understand the technology well can narrow that uncertainty significantly and make far more informed decisions. At the same time, everyone should learn to codeor at least learn to build software with AI. AI has lowered the barrier to creating custom tools. Today my marketers, recruiters, HR professionals, and financial analysts who use AI to write code are already more productive than those who do not. When I hire, I increasingly prefer people who know how to build with AI assistance. I may have been early on this shift, but I now see more startups and established companies moving in the same direction. Just as it became unthinkable to hire someone who could not search the web or use email, I am already at the point where I hesitate to hire knowledge workers who cannot use AI to build or automate with code.
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E-Commerce
Early in my career, a colleague and I made a shared commitment one summer to eat healthier. Salads. Smoothies. The full routine. Like many well-intentioned plans, our discipline began to fade after a few weeks. Eventually, we introduced what we jokingly called Grease Wednesdays, a weekly cheat day as a reward for all our good behavior. Every Wednesday, one of us would head out to grab fast food, and wed hide away in a small boardroom to indulge in our shared lack of nutritional discipline. At first, it was just the two of us, chatting with laptops closed and fries on the table. And then coworkers began peeking into whatever boardroom we were in, curious about the laughter. Eventually, someone asked if they could join. Then another. Within weeks, we had outgrown the small meeting room. Within months, we had moved into the departments largest boardroom to accommodate the growing crowd. What started as a casual indulgence became a shared ritual. And without intending to, Grease Wednesdays began to change our department culture. We all began to get to know each other as individuals, with pets and families and hobbies. The ritual also smoothed tensions between departments, built friendships between unfamiliar teammates, and helped us realize we hadnt felt all that connected before. Recent research shows the disconnection I witnessed in my own team is now part of a broader workplace trend. A 2025 survey of U.S. workers found nearly 40% report feeling lonely at work, and employees who lack social connection are significantly more likely to consider leaving their jobs because of it. When people feel they belong, trust builds, collaboration accelerates, performance rises, loyalty deepens, and well-being improves. When they dont, silos form, trust erodes, and discretionary effort fades. Take these numbers: a recent BetterUp survey found that workplace belonging leads to a 56% increase in job performance, a 50% reduction in turnover risk, and a 75% decrease in employee sick days. THE PROBLEM WITH OVER-ENGINEERING CONNECTION Belonging is not accidental; its cultural. And culture is shaped, reinforced, and protected by a leaders vision, values, behavior, and accountability, including what I call positive accountability. But this is where many organizations misstep. When leaders notice disconnection, the instinct is often to formalize solutions with more engagement meetings, structured team building, and mandatory social events. Yet forced connection and fun rarely produce authentic trust. In fact, over-engineering connection can make people more guarded. For instance, research cited in a study by the University of Sydney found that when team-building activities feel mandatory, they can create resentment and pushback among employees. Belonging grows best in environments that feel natural, voluntary, and human, not observed or measured. If you want to improve connection and belonging in your workplace while avoiding forced connection, here are some steps you can take. DESIGN INTENTIONAL SPACES What made Grease Wednesdays powerful wasnt the food. It was the opportunity that a casual ritual created. We had, quite by accident, built a small, repeatable, low-pressure interaction in which familiarity could grow.Design offers a strong middle ground between compulsory team-building exercises and complete social neglect. The key here is to design small, optional, and repeatable opportunities that humanize the workplace. For in-person teams, you can host walking one-on-one meetings, Friday coffee drop-ins, no-agenda team lunches, or cross-department donut runs. For remote teams, you could host 15-minute morning online coffee drop-ins or no-agenda team virtual lunches, and share team celebrations of birthdays, anniversaries, and project completions. Keep it light; keep it optional; keep it ritual. MODEL OPENNESS Studies in organizational research find that when leaders are open, available, and accessible, employees feel more psychological safety. Psychological safety, coined by organizational psychologist Amy Edmondson, is the shared belief within a team that it is safe to take interpersonal risks, like speaking up with ideas, questions, concerns, or mistakes, without fear of punishment, humiliation, or retribution.To build psychological safety in teams, leaders can model openness. Do that by admitting when you dont know something, sharing a decision youve reversed (and why), and publicly thanking a team member who challenged you. Another way you can model openness is by offering positive team accountability by sharing the successes they see and are proud of within the team. For example, one leader I work with sends out an email to his team every two or three weeks. The irregularity of timing is actually effective by design, making the email feel more authentic. REWARD CONNECTION, NOT JUST OUTPUT Social psychology research shows that reciprocity in the workplace builds trust, cooperation, and positive relationships. The principle of social reciprocity, or when one recognizes and responds to positive actions, contributes to stronger workplace dynamics and mutual respectthe core components of connection and belonging.One way to do this is to shift what gets publicly praised. If the only Slack shout-outs are for revenue, speed, and delivery, people will assume that is all that matters. Instead, reward connection by recapping projects in team meetings by asking, Who helped make this possible? You can also celebrate the people who mentor, unblock, and build bridges across teams. When helping behavior is acknowledged, rewarded, and career-relevant, connection stops being invisible labor and becomes part of how success is defined. Full offices dont cure loneliness, but intentional culture does. When leaders design natural rituals, model openness, and reward connection as deliberately as they reward performance, belonging is no longer accidentaland becomes part of how work actually works.
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E-Commerce
Being a middle manager often feels like living in two worlds at once. On one side, executives cascade big goals and sweeping strategies. On the other, teams look to you for clarity, advocacy, and daily guidance. Youre constantly reconciling top-down demands with bottom-up realities, often with too little time and too few resources to satisfy either side. The paradox of the role is stark: Middle managers carry enormous responsibility for execution but dont always have the authority to make critical decisions. Youre expected to deliver results on budgets you dont control, within structures you didnt design, and through policies you didnt write. This tension is one of the biggest sources of chronic strain. One survey found that middle managers reported higher burnout rates (36%) than non-managers, while another showed that 71% are sometimes or always overwhelmed at work. But heres the good news: The middle isnt just where pressure piles up. Its also where strategy becomes reality, where culture is lived (or lost), and where agility gets tested in real time. If you can reframe the squeeze as an opportunity, middle management becomes less a grind and more a proving ground. Here are four ways to turn the pressure into potential: BUILD YOUR COALITION If you think of your team only as your direct reports, youre missing the larger playing field. Work today is inherently cross-functional, which means your effectiveness hinges on your ability to influence sideways and upward, not just to manage downward. Peers hold the resources and expertise you need. Leaders above you control priorities, approvals, and air cover. Without credibility in those directions, even flawless execution within your own group can collapse at the edges. Research shows that misalignment between teams is one of the biggest drivers of wasted work. When priorities or interpretations differ, teams can spend weeks pulling in opposite directions. Middle managers who proactively build peer alignment surface these gaps early and save everyone time and frustration. The fix isnt complicated, but it is intentional: cultivate your network. A short, well-timed conversation with a peer or senior leader can prevent the kind of breakdowns that leave your team spinning. Think of it less as networking and more as preemptive damage control. The middle managers who thrive are the ones who invest in relationships that make the work move. MASTER THE PRACTICE OF LEADERSHIP Leadership is often packaged as a set of sweeping competencies or treated like a fixed trait you either have or dont. In reality, leadership is shaped over time, forged through daily choices, interactions, and repeated practice. While traditional leadership development focuses on broad skills taught in workshops or courseswhat we call horizontal development at Sounding Boardmany real-world challenges require something deeper. Vertical development helps managers think more complexly, adapt to evolving contexts, and lead with lasting impact, not just quick fixes. This kind of development happens through practice, not theory. Neuroscience supports it: Consistent, real-world repetition strengthens the neural pathways that anchor adaptability and retention. At BTS, weve seen that transformational leadership often hinges on unlocking specific mindset shifts, patterns where leaders typically get stuck and need to evolve to grow. So, how do you start? Find smaller moments to experiment. Instead of waiting for a performance review, try a quick debrief after a call with a direct report. Test a new communication approach in a team meeting before the next town hall. You can even name your intention to those around you. Letting others know youre trying something new sets expectations and invites helpful feedback. LEVERAGE AI FOR ON-DEMAND SUPPORT Your toughest challenges dont show up as theory; they show up in the form of messy, human situations: a disengaged direct report, a senior leader who keeps moving the goalposts, a peer who wont align. These problems dont have one-size-fits-all solutions, which is why coaching is so powerful. For decades, personalized coaching was a privilege reserved for executives. But with AI practice bots paired with guidance from real coaches, middle managers can get development thats personalized and scalable when they need it. These tools let you rehearse tough conversations, like giving feedback or delegating more effectively, in a low-stakes environment. Coaches help you translate insights into actions and longer-term mindset shifts. The result is leadership growth thats less abstract and more actionable. The smartest move? Start small. Pick one conversation youve been avoiding and rehearse it with an AI conversation bot. Youll uncover blind spots, test new approaches, and walk into the real thing with more confidence and control. MAKE UNCERTAINTY YOUR PLAYGROUND The defining condition of modern work is uncertainty. Markets swing, technologies disrupt, priorities pivot. If you wait for clarity, youll always be behind. The managers who thrive arent the ones who resist ambiguity, but those who use it as a catalyst to experiment and learn. One biopharmaceutical company I worked with recognized this when it expanded leadership development beyond senior executives to include middle managers. After providing leadership training focused on managing ambiguity and integrating AI into workflows, the company paired each manager with a coach to help translate learning into action. The result was faster decision-making and stronger cross-functional collaboration during a major pivot. When you stop treating uncertainty as a threat and start treating it as a laboratory, you shift from surviving change to shaping it. With these practices, middle management isnt a burden, but a launchpad for growth.
Category:
E-Commerce
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