Everyone is talking about longevity.

In the volatile world of manufacturing, one factor is emerging as the ultimate differentiator in 2026: speed, and in particular, operational velocity. This is the ability to sense market changes, make decisions quickly and decisively, and recover swiftly across the entire value chain.

In 2014, my husband came home from a run one day with the crazy idea that we should make an at-home insemination kit. We had zero experience in the fertility industry, no MBA, no connections, and not much money. But we had one important element: first-hand user experience. We knew personally that this product was greatly needed. We had looked for it, and never found it.

Tech CEOs are eager to blame AI for mass layoffs. But a recent Gallup poll shows that only 1% of laid-off workers agree.

Eugenia Kuyda remembers when one of her favorite apps became too bloated. All she wanted was a simple weight tracker. But she also got calorie counting, meal tracking, and a subscription she didn’t need.

AI agents are only as powerful as the models behind them, which must process vast amounts of data. No company is more vital in helping process that data than Nvidia.

Robots and AI agents are still limited when it comes to learning new things. That’s exactly the problem that fascinates Chelsea Finn, a Stanford computer science and engineering professor. Her research focuses on giving autonomous systems frameworks to reason through complex tasks.

While some companies are still exploring AI, Daniel Danker cites where it’s already working at Walmart. “We see it as the technology to solve a bunch of problems for customers,” says Danker, the retail giant’s executive vice president for AI acceleration.

AI agents come with real risks: The more autonomous they are, the more likely they are to violate data privacy rules or international SPAM rules, and the easier they become for hackers to attack. But until recently there was no standard for measuring these risks and no entity to insure against them. Meanwhile, enterprises will increasingly need to trust AI agents to do real work. Rajiv Dattani’s Artificial Intelligence Underwriting Company (AIUC) has begun insuring agent providers against these risks.

Elizabeth Minor has been thinking about the risks of agentic AI and, specifically, autonomous weapons, for years. Minor is the head of policy at Stop Killer Robots, a coalition founded in 2012 in order to ensure that humans maintain control over technology in times of war. That coalition has grown to represent more than 300 organizations in more than 70 countries, including Human Rights Watch and Amnesty International.

At Google DeepMind, Lila Ibrahim occupies a role that didn’t previously exist: chief AI readiness officer, focused on how to prepare the world for rapidly advancing artificial intelligence. She helps governments think about policy, boosts public understanding of AI, and works to ensure that Google deploys AI responsibly.

After seeing how coders have been delegating large parts of their jobs to AI, Charles Lamanna wants office workers to start doing the same. Lamanna, Microsoft’s executive VP of agents and business apps, heads Copilot Cowork, which lets workers hand off complex tasks to AI agents that hook into Microsoft’s 365 suite. While it’s still in a limited research preview, Cowork allows them to do things like build competitive analyses from internal Excel spreadsheets or weed out scheduling conflicts using context from Outlook.

Tomorrow.io’s “weather intelligence” platform started with a focus on better data and eventually grew to include the company’s own constellation of satellites to cover the globe in detail, sampling every point on the planet roughly once an hour. But the CEO, Shimon Elkabetz, saw the opportunity to go farther and use agentic AI to help customers evaluate specific weather risks.

Cameron Stanley, a Pentagon veteran who returned to government after a stint at Amazon Web Services, is pushing to make the Defense Department an “AI-first organization,” from back-office logistics to the battlefield. Speed is the edge: “The right decision, the fastest” wins, he says. In recent months, he has overseen the launch of the GenAI.mil platform, now used by nearly 2 million personnel, while another tool, Agent Designer, saw more than 10,000 agents built in its first three days to address tasks and workflows. Even four-star generals are fluent enough that “they’re now testing me,” he says.

AI-powered apps can’t always afford to serve their customers. “Product market fit does not equal a viable business,” says Lin Qiao, CEO of Fireworks AI. “If startups aren’t careful, they can scale into bankruptcy.”

Until recently, Peter Steinberger was a developer working in relative obscurity. But in November 2025, he launched the viral AI agent platform OpenClaw. Within a few months, he was receiving a hero’s welcome at a San Francisco OpenClaw event where 1,300 people signed up for a 500-capacity space—a crowd that felt closer to one of Apple’s famously buzzy launches.

When Swami Sivasubramanian joined Amazon as a technical intern in 2005, cloud computing was just getting off the ground as a concept. Thanks to Amazon Web Services, it grew into one of technology’s most essential ingredients—and Sivasubramanian, who kept rising through the ranks, helped drive that change. In 2017, he became AWS’s VP of AI.

Cyberattacks are increasingly making use of artificial intelligence, using the technology to target organizations at scale. To fight back, the corporate cybersecurity teams that track, monitor, and respond to these threats may need to start using AI a lot more too. Or at least that’s what Edward Wu argues. He’s the CEO of Dropzone AI, which has developed a large language model-based digital cyberthreat investigator. 

As AI models move into a more agentic era, shifting from chat to completing tasks, they’ll be making real and increasingly consequential decisions.