Eric Jing: I spent 20 years in search, beginning at Microsoft and helping launch Bing. What I learned is simple: People do not search because they want more information. They search because they want to get something done. That insight became the foundation of Genspark. We are building AI to help people complete work—not just help them find information.

But as AI exploded—new models, new tools, new capabilities every few weeks—a new problem emerged. Most knowledge workers we talked to were spending more time evaluating AI than using it. The very thing meant to make people more productive was becoming another full-time job. That complexity is becoming a job of its own—and it’s not a problem most people should have to solve.

We track which models perform best for which tasks, orchestrate them behind the scenes and deliver results that any knowledge worker can use without needing technical expertise.

Think of Genspark as an AI warehouse club: one membership, one place that brings together the best models, tools and capabilities in a single workspace. Instead of constantly evaluating vendors, switching platforms or becoming an AI expert yourself, you can simply describe what you need and get the work done.

Jing: What holds many people back is not a lack of interest in AI. It is the feeling that AI is hard to learn, changing too quickly and difficult to trust. Questions around data security are valid. As a result, most people still use AI in a very limited way, as a faster search engine or a basic chatbot.

But the technology has already moved far beyond that. Today, anyone can use a phone to direct AI agents to work on their behalf 24/7. A product like Genspark Claw makes that shift easier to picture: a cloud-based agent with its own computer environment that can keep working in the background, more like a digital operator than a chatbot. Instead of asking a quick question and getting a quick answer, you can assign an agent to research a market, analyze competitors, build recommendations and carry out complex tasks while you focus on higher-value decisions. When you come back, the work has already progressed. That is a fundamentally different way of working.

That is how AI begins to extend individual reach, and how an AI-native organization takes shape—not from a top-down redesign but from the bottom up, as individuals learn to work with AI as part of their team.

A new kind of role emerges in the process: the AI specialist. Not just engineers, but marketers, consultants, salespeople and operators who can now accomplish work that once required much larger teams. It is not a job title so much as a new ceiling on what one person, working alongside AI, can do. The most valuable employees going forward will not be the ones who spend the most hours at their desks. They will be the ones who adapt fastest and turn these systems into real leverage for the business.

Jing: I’ll be honest about what’s still hard. Three things, in particular.

First, the economics. Token costs and ROI don’t always line up. The most capable models are also the most expensive, and not every workflow justifies the price. Getting the math right, knowing when to use a frontier model versus a lighter one, is a real engineering and product discipline, not a given.

Second, data boundaries. Every enterprise leader I talk to asks the same question: Where does my data go, and could it end up training someone else’s model? That concern is legitimate. The industry is still establishing the norms, contracts and technical guarantees that make this verifiable, not just promised.

And third, the hardest one, adoption. The tools have moved faster than the people using them. I can deploy a powerful system to a 5,000-person company on Monday, and by Friday only a few hundred employees are actually using it well. Getting an entire team to genuinely change how they work is harder than building the technology. It’s a leadership problem, not a software problem.

We’re learning, in real time, where AI is ready to be trusted and where the gaps still are. At Genspark, we’ve built our platform around all three problems: routing across the full lineup of frontier models to balance capability with cost, designing an enterprise-ready product with security in mind from day one, and obsessing over a user experience simple enough that adoption tends to happen on its own. The technology keeps moving, and none of this is solved forever. The companies pulling ahead are not the ones pretending otherwise. They are the ones honest enough to name the gaps and disciplined enough to close them.

Jing: What we have seen is that tangible gains do not happen without leadership involved directly. AI adoption cannot be delegated to IT or innovation teams alone. Leaders understand where the business is constrained, which workflows matter most and where better execution translates into growth. They have to use these systems firsthand and set the pace by understanding what the tools can actually do.

Just as important, companies have to be deliberate about which generation of AI they are adopting. Two products may both be called AI but belong to very different generations. Older tools can become last-generation systems surprisingly fast. That is simply the nature of the AI cycle. The companies moving fastest are adopting the newest platforms early and scaling them broadly. When leadership pairs that judgment with broad employee adoption, AI stops being a side experiment and starts becoming a capability layer across the entire business.

Jing: This issue matters a great deal, but the landscape is improving quickly. Newer AI systems are much better equipped to address enterprise concerns around security and governance. With products like Genspark Claw, each agent runs inside a dedicated virtual machine, a setup enterprises already understand. That gives organizations familiar ways to monitor activity, apply security software, see how data is flowing and control what the AI can access.

That makes governance much more practical. Companies can define what data an agent can use, what tools it can leverage and what actions it is allowed to take. The goal is not uncontrolled autonomy. It is to give enterprises AI systems they can supervise, secure and integrate into existing governance frameworks with confidence.

Some hesitation remains because AI is evolving so quickly. But increasingly, the fear comes more from the speed of change than from a lack of workable controls. On security and governance, the newest platforms have made major progress and they are improving fast.

Jing: Genspark’s story is still being written, and we are still a small company. But I have a concrete basis for comparison. Before this, I served as a chief product officer and VP managing organizations of several thousand people. Having operated in both environments, I can say firsthand: The structure of work is changing. With AI, small teams can move faster, operate with far more leverage and execute work that once required much larger organizations. Genspark is an early example of what that looks like, not a template every company should copy, but proof of what is possible.

That shift is only beginning. AI systems are becoming more autonomous, their ability to automate work is rising quickly, and the ways enterprises can govern and control them are growing more sophisticated. We are already seeing ordinary knowledge workers operate with 10 to 100 times the effectiveness of traditional workflows. What was theoretical a few years ago is happening now.

AI is a revolutionary technology, and no one, including us, yet knows exactly where it leads. What I can say is that the early results are already hard to ignore.

An AI-native organization is not defined by when it started, but by how it works. AI is treated as part of the team, not a tool bolted on the side. Structures are flatter, teams leaner, cycles faster. That is the AI specialist in practice, and that kind of organization is already taking shape.

Some companies will be built this way from scratch. Most will get there by evolving, function by function, team by team. Both paths are already underway.

At Genspark, we are building one version of this and making the tools that let organizations of every size build their own. That is the work. That is the era. And it is just beginning.

Learn more about how Genspark can help your organization work with AI more effectively.