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Marco Argenti: We Must Prepare AI Natives to Shape the Future of Work

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This opinion article was originally published in Fortune on July 3, 2025.

 

Agentic AI is driving a monumental, generational shift that is poised to revolutionize industries and reshape workforce dynamics in ways we are only beginning to understand. Soon, human and AI “workers” will learn to coexist, collaborate, and thrive together. The path to that future, and the success of this collaboration, will depend on the next generation of talent leading the way.

Agentic AI refers to artificial intelligence systems that can perform tasks on behalf of humans and make independent decisions without direct oversight. These systems can reason based on context, memory, and available data, generate detailed plans, and autonomously execute the steps required to complete a task. Their growing capabilities mark a shift from passive tools to active collaborators.

While some speculate that agentic AI will displace many junior-level roles—and there may well be a certain level of recalibration—the reality is more nuanced. Rather than diminishing the importance of early-career workers, this shift makes them more critical than ever for one simple reason. The generation now entering the workforce has “grown up” alongside generative AI. They are more comfortable with its pace and equipped to shape its future. They are “AI natives.”

At the same time, as someone famously said, “there’s no compression algorithm for experience,” and experience and sound judgement are not intrinsically an attribute of generative AI, which at best is 4 years old and still undergoing rapid evolution. Which begs the question: Who’s going to provide experienced supervision to a potentially limitless number of AI agents entering the workforce?

Understanding how we nurture a generation of AI natives—and equip them with the right skills and tools to be leaders and not passive observers of this transformation—will be critical to defining the future of work and society at large. Their instincts, creativity, and adaptability will determine how successfully we integrate AI into our organizations not just as a tool but as a partner. The challenge ahead is beyond technological; it is cultural, educational, and distinctively human.

The new AI paradigm

Here’s the first thing we need to come to terms with: This is a new game with new athletes who are likely more proficient than previous players ever will be. 

Think of it this way: If you’re asked to learn the piano later in life, you might be enthusiastic and dedicated, but the odds of becoming a prodigy are slim. Similarly, think about someone who learned to use a computer well into adulthood. Even decades later, their typing, mouse usage, or navigation of user interfaces often reveals their late start.

The same dynamic is now unfolding with AI tools. A generational divide is emerging—not because more seasoned professionals lack intelligence or drive, but because they didn’t grow up with these tools. For those who aren’t AI natives, adapting to an AI-first or AI-hybrid workforce may prove more difficult than we anticipate. However, that’s where most of the institutional knowledge and experience lies.

Several technological shifts have created similar knowledge vacuums: the introduction of computers, the internet, mobile, cloud technologies, and others. In each case, it took a decade or more before fluency became a baseline requirement for certain roles. Those who couldn’t adapt either transitioned into roles that didn’t require those skills or exited the workforce altogether. What’s different now is the speed. The AI shift is happening in years, not decades. Workers who lack proficiency in leveraging AI tools will fall behind, and those who have learned to harness it to elevate their work will advance.

As with every major technological shift, a new generation of leaders is emerging, particularly entrepreneurs whose native fluency with AI is reshaping the landscape. Consider the CEOs of companies like Devin, Windsurf, and Scale AI—all AI natives. Could one of them be the next Bill Gates or Michael Dell? It’s possible. Our responsibility as a society and as leaders is therefore twofold: to maximize the potential of this new generation of AI natives, and to ensure the rest of the workforce focuses on accelerating the “path to seniority” for our junior athletes.

Investing in AI natives

Our priority must be to invest in junior talent who will redefine the industries we work in. While the exact contours of this transformation are difficult to predict, its scale is easy to imagine if we accept that AI is the most profound technological disruption of our time. In a world where technology evolves at sonic speed, our focus must be on ensuring that human adaptation keeps pace. Simply put, we need to train our best athletes for this new arena and equip them with the essential skills to manage and lead this change in an accelerated way.

With the arrival of agentic AI, the ability to spin up AI coworkers on demand will soon be a baseline capability. This shift will require even the most junior employees and individual contributors to master three foundational management skills: describing a task clearly, delegating it effectively to an AI agent, and supervising the results. Supervision is especially critical in a world where agent technology is still maturing. The failure mode here is not technological, it’s organizational. Delegating work to an agent without the ability to supervise it is a recipe for disaster, which is why we must instill a new sense of quality control and agency in our people.

As an example, AI systems today are highly sensitive to how questions are posed. The prompt or “context” is processed by the AI’s attention layers, which determine the relative importance of each word or token. A slight miscommunication can be amplified, distorting the output. In the case of autonomous agents, hallucinations don’t just lead to bad answers, they can trigger incorrect or even dangerous actions. Until we are confident these tools will not act irrationally, we must keep humans in the loop. Therefore, rethinking the concept of agency is essential.

Agency, in this broader sense, includes the tasks delegated to an AI agent, how those tasks are executed, and how the agent communicates with humans, data sources, and other agents. New communication protocols like MCP and A2A are emerging to standardize these interactions. But the human role remains central.

As junior employees take on the responsibilities of “managers,” the traditional boundaries between business and engineering are dissolving. Much like how product managers and engineers have converged, today’s professionals must be fluent in both domains. To be a great engineer now means also being a great product manager: understanding the customer, defining the roadmap, identifying risks and biases, and designing compensating controls. This is the mindset we must cultivate in our AI-native workforce. They will be expected to manage their AI agents not just by issuing commands, but by understanding their capabilities and limitations, and by anticipating risks before they become problems. Supervision is key, which requires experience, and experience requires time—which, at this pace of change, is a scarce commodity. 

Supervision is key to this evolution. We must ensure that the one who delegates has the ability to check the quality of the work being created by an AI. Imagine an airline that, because of the introduction of the autopilot with auto-land and auto-take-off features, decides to fill some of the flights with only junior pilots. Would we sense the same level of safety and quality control? Only if we felt the junior pilots were properly equipped to supervise.

Ultimately, cultural transformation in a period of such sharp technological advancement is about more than adopting new tools. It is about forming a new generation of leaders and accelerating their path to experience, equipping them with managerial skills from the outset, and leveraging their innate familiarity and proficiency with this new technology. 

Today, technology change is ahead of human change. It’s easier to change software and AIs than it is to rewire the human brain, to break old habits and create new skills. Non-AI natives—most of us—have possibly the most challenging task of all: to pass the baton to a new generation of humans entering the workforce and equip them with the skills necessary to manage something that the current generation does not fully understand. All this, without the luxury of time.



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Smart medicine: Artificial intelligence reaches the health fund – The Jerusalem Post

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Smart medicine: Artificial intelligence reaches the health fund  The Jerusalem Post



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Teachers Training on AI

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MOBILE, Ala. (WALA) – Some leading tech companies are investing millions to train teachers on how to use artificial intelligence. The $23 million initiative is backed by Microsoft, OpenAI, Anthropic, and two teachers’ unions. The goal is to train 400,000 kindergarten through 12th-grade teachers in artificial intelligence over the next five years. The National Academy of AI Instruction announced the effort. The group states that it will develop an AI training curriculum for teachers that can be distributed online and at an in-person campus in New York City.

The announcement comes as schools, teachers, and parents grapple with whether—and how—AI should be used in the classroom. Educators want to ensure students know how to use a technology that’s already transforming workplaces, while teachers can use AI to automate some tasks and spend more time engaging with students.

Samsung unveils its new line of foldable devices at Unpacked

The future is here—Samsung is showcasing its future-ready smartphones! Check out the new Galaxy Z Fold 7 and the Z Flip 7 taking center stage at the company’s latest Unpacked event. The Korean electronics company unveiled the upgrades, including new versions of its watch, and also announced an expanded partnership with Google to inject more artificial intelligence into its foldable lineup. For example, users can access AI by speaking to their watch! Oh, and yes… it also tells you the time.

The Fold 7 will retail starting at $1,999. Pre-orders start today, and the device will hit shelves on July 25.

The Galaxy Z Flip 7 will retail for $1,099.99 and the Flip 7 FE starts at $899.99. Pre-orders for both devices began Wednesday and both will be available generally on July 25.



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AI vs Supercomputers round 1: galaxy simulation goes to AI

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Jul. 10, 2025
Press Release

Physics / Astronomy


Computing / Math

In the first study of its kind, researchers led by Keiya Hirashima at the RIKEN Center for Interdisciplinary Theoretical and Mathematical Sciences (iTHEMS) in Japan, along with colleagues from the Max Planck Institute for Astrophysics (MPA) and the Flatiron Institute, have used machine learning, a type of artificial intelligence, to dramatically speed up the processing time when simulating galaxy evolution coupled with supernova explosion. This approach could help us understand the origins of our own galaxy, particularly the elements essential for life in the Milky Way.

Understanding how galaxies form is a central problem for astrophysicists. Although we know that powerful events like supernovae can drive galaxy evolution, we cannot simply look to the night sky and see it happen. Scientists rely on numerical simulations that are based on large amounts of data collected from telescopes and other devices that measure aspects of interstellar space. Simulations must account for gravity and hydrodynamics, as well as other complex aspects of astrophysical thermo-chemistry.

On top of this, they must have a high temporal resolution, meaning that the time between each 3D snapshot of the evolving galaxy must be small enough so that critical events are not missed. For example, capturing the initial phase of supernova shell expansion requires a timescale of mere hundreds of years, which is 1000 times smaller than typical simulations of interstellar space can achieve. In fact, a typical supercomputer takes 1-2 years to carry out a simulation of a relatively small galaxy at the proper temporal resolution.

Getting over this timestep bottleneck was the main goal of the new study. By incorporating AI into their data-driven model, the research group was able to match the output of a previously modeled dwarf galaxy but got the result much more quickly. “When we use our AI model, the simulation is about four times faster than a standard numerical simulation,” says Hirashima. “This corresponds to a reduction of several months to half a year’s worth of computation time. Critically, our AI-assisted simulation was able to reproduce the dynamics important for capturing galaxy evolution and matter cycles, including star formation and galaxy outflows.”

Like most machine learning models, the researchers’ new model is trained using one set of data and then becomes able to predict outcomes based on a new set of data. In this case, the model incorporated a programmed neural network and was trained on 300 simulations of an isolated supernova in a molecular cloud that massed one million of our suns. After training, the model could predict the density, temperature, and 3D velocities of gas 100,000 years after a supernova explosion. Compared with direct numerical simulations such as those performed by supercomputers, the new model yielded similar structures and star formation history but took four times less time to compute.

According to Hirashima, “our AI-assisted framework will allow high-resolution star-by-star simulations of heavy galaxies, such as the Milky Way, with the goal of predicting the origin of the solar system and the elements essential for the birth of life.”

Currently, the lab is using the new framework to run a Milky Way-sized galaxy simulation.

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Reference

Hirashima et al. (2025) ASURA-FDPS-ML: Star-by-star Galaxy Simulations Accelerated by Surrogate Modeling for Supernova Feedback. Astrophys J. doi: 10.3847/1538-4357/add689

Contact

Keiya Hirashima, Special Postdoctoral Researcher

Division of Fundamental Mathematical Science, RIKEN Center for Interdisciplinary Theoretical and Mathematical Sciences (iTHEMS)

Adam Phillips
RIKEN Communications Division
Email: adam.phillips [at] riken.jp

The simulated galaxy after 200 million years. While the simulations look very similar with and without the machine learning AI model, the AI model performed 4 times as fast, completing large scale simulation in a matter of months rather than years.







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