When AI shapes young minds: The cognitive risks of early, unfiltered use

Artificial intelligence is reshaping how children learn. From homework helpers to instant tutors, AI is present earlier in childhood than many of us expected. That access brings benefits: faster feedback, new explanations, and new ways to practice. It also brings a less obvious risk, one that quietly changes how young people learn to think.

Why this matters

Learning is not only about acquiring facts. It is the process of building mental tools: the patience to wrestle with a hard question, the persistence to try multiple approaches, and the habit of testing an idea by making small mistakes. When those habits are replaced by instant answers, the brain misses crucial training.

The cognitive risks of early, unfiltered AI use

• Weakened problem-solving muscles. Reasoning strengthens when learners try, fail, and adjust. Instant solutions can short-circuit that cycle.
• Lower tolerance for ambiguity. If an answer is always a click away, children may stop learning to hold a question and explore multiple possibilities.
• Surface-level understanding. AI can produce correct outputs without the student internalizing the underlying logic.
• Convergence of thought. AI trained on large data can subtly nudge many students toward similar reasoning patterns, shrinking diversity of approaches.
• Dependency and decreased curiosity. Over-reliance on tools encourages seeking quick answers rather than asking deep questions.

Not all AI use is harmful “context and design matter”

AI is a tool. It can accelerate discovery when used to extend human effort rather than replace it. The risk appears when children use AI as a substitute for thinking, not as a partner for it.

Good use: AI offers hints and nudges while the learner still does the core work. Harmful use: AI supplies full solutions that the learner copies without reflection.

Practical rules for parents and teachers

Here are clear, practical steps to help children benefit from AI while protecting cognitive growth.

• Require first attempts: Ask students to show their own thinking before using AI. A short sketch, rough notes, or a recorded explanation is invaluable.
• Use AI as a coach, not a copier: Configure tools to provide hints, not final answers. Prompt children to try once, then request a hint if stuck.
• Teach verification skills: Show how AI can be wrong and how to check outputs using logic, examples, or trusted references.
• Encourage reflective prompts: After using AI, have the student explain in their own words what changed and why.
• Limit easy access during practice: For tasks designed to build thinking, discourage AI use until the student has practiced independently.
• Mix human feedback with AI feedback: Human coaching that focuses on process, not just correctness, preserves cognitive development.
• Prioritize oral examinations for accurate assessment: Unlike multiple-choice tests or project-based evaluation, oral exams create a direct exchange that eliminates guesswork and minimizes opportunities for cheating. They also reveal each student's genuine understanding, depth of reasoning, and individual contribution.

Curriculum and policy suggestions

Schools and districts should not only adopt technology; they must define how it is used. Practical policies include:

• Designated periods for “AI-free practice” during which students must work without assistance.
• Assignments that require process documentation (drafts, logs, or audio reflections).
• Teacher training on how to scaffold AI as a learning partner.
• Assessment methods that reward reasoning steps, not just final answers.

How to explain this to kids

Simple language works best: “AI can be a smart friend, but friends shouldn’t do your homework for you. practicing and making mistakes helps your brain grow.” Build small rituals: try 15 minutes alone, then 10 minutes with a tool, then five minutes to explain what you learned.

Quick checklist for today:

1. Ask your child to do one homework problem without tools.
2. Discuss the steps they took.
3. Then invite them to use an AI assistant for hints and compare results.

Conclusion

AI will continue to be a helpful presence in children’s lives. The choice we face is not whether to use it, but how. If we design learning environments that force thinking before assistance, teach students to verify and reflect, and pair AI with human guidance, we can preserve and even strengthen the core habits of reasoning that last a lifetime.

Why entrepreneurs are joining the Microsoft Excel world championship

Why entrepreneurs are joining the Microsoft Excel world championship

Most people think of Excel as a simple tool for budgets or reports, the kind of software that hides somewhere on a crowded desktop. Yet every year, something unexpected happens: Excel steps into the spotlight. Not as a productivity app, but as a full-scale competition watched by thousands around the world. Yes, an actual world championship where speed, logic and creativity collide in thirty intense minutes of problem solving.

It may sound surprising at first, but the atmosphere is closer to an esports arena than a quiet office. Competitors face fast-paced scenarios where they must recreate shapes, build models, break down logic puzzles or combine formulas with astonishing precision. Every five minutes, the lowest score is eliminated. The pressure is real, the energy is electric and the crowd reacts to formulas the same way others react to a perfect chess move or final-second goal.

How the competition works

Throughout the year, players join online challenges that serve as gateways to the main event. These sessions are open to anyone and give newcomers a taste of the intensity behind Excel esports. The best competitors move forward to a playoff structure, and only a small group earns a seat at the live finals in Las Vegas, where a packed audience watches them think faster than most of us type.

What truly stands out is that the finalists are far from anonymous. Many are founders, consultants or business owners who spend their days solving real problems for real clients. Andrew from Australia, Michael from Canada, Di from Ireland, their profiles look more like LinkedIn success stories than stereotypical gamers. And yet, when the countdown begins, they become athletes of logic, transforming spreadsheets into a field of strategy and instinct.

Why entrepreneurs belong in this arena

This championship is more than a contest; it is a reminder of what modern leadership looks like. Today’s founders navigate data, automation, financial models and rapid decisions every single day. Mastering the tools behind those decisions is not optional anymore. Excel may seem simple at first glance, but at high level it becomes a playground where creativity meets discipline.

When entrepreneurs step into this competition, they send a strong message to their teams and clients: “I understand the tools we use. I can think fast, adapt quickly and solve problems under pressure.” There is something refreshing, even inspiring, in seeing business leaders roll up their sleeves and compete alongside analysts and students. It breaks the myth that CEOs only delegate technical work. Here, they prove they can lead through skill, not only through title.

A path open to anyone willing to try

You do not need to be a genius or memorize every function to get started. The championship welcomes people at many levels. The online battles offer a chance to learn, experiment and progress. By repeating challenges, you slowly build the intuition that the best players share: the ability to see patterns, think in formulas and transform uncertainty into structure.

Whether you are an entrepreneur, a student or someone simply curious, this competition offers a rare opportunity to join a community that values intelligence, curiosity and technical mastery.

Discover the competition on the official Excel Esports website

A final word

At its core, entrepreneurship has always been a race against time. You identify a problem, design a solution and adjust faster than everyone else. The Excel World Championship celebrates exactly that mindset. It transforms spreadsheets into a story of resilience, strategy and passion.

If this world inspires you and you want to strengthen your technical abilities even further, you may enjoy one of my LinkedIn Learning course dedicated to combining the power of Python with Excel. It is designed to help professionals automate tasks, analyze data more effectively and bring their spreadsheet skills to the next level.

Agentic AI: Agentic AI and the future of work (Episode 4)

Agentic AI will accelerate automation, not just of simple, repetitive tasks, but of many complex workflows that once felt untouchable. That reality is both unsettling and full of opportunity. The question that matters is not whether change will happen, but how we steer it so people and organizations thrive.

Accepting a new scale of automation

It’s helpful to be frank: autonomous agents will take on a huge volume of repetitive work and many complex tasks previously seen as uniquely human. Some professions will change dramatically, others may shrink or disappear. That’s a hard truth and a moment to plan rather than panic.

Social safety and the case for shared security

Given the scale of transformation, ideas once considered radical like a universal basic income or income smoothing mechanisms are worth serious discussion. These are the kinds of social tools that can buy time for reskilling and reduce the human cost of rapid disruption.

New jobs may emerge

History shows us that technological revolutions destroy some jobs and create others. Agentic AI will spawn new roles: agent designers, orchestration engineers, AI ethicists, interaction designers for human–agent teams, and jobs we can’t yet name. The net effect depends on how we train and transition talent.

Why humans still matter: Innovation, Values and Empathy

Even when we tweak model temperature to drive creativity, we remain inside the box operating with constraints of data, assumptions, and design choices. Humans are essential for going truly out of the box: imagining new problems, reframing goals, and ideating radical directions that machines cannot originate on their own.

Beyond ideation, humans carry a bedrock of values. Empathy, cultural understanding, and moral judgment are the lenses through which we sense evolving customer needs and design services that matter. Those human qualities are not optional; they are the glue that makes technological capability humane and useful.

On layoffs, short-term gains, and long-term regret

Some companies may see productivity gains and respond by massively cutting headcount. That path risks long-term damage. An employee augmented with AI can reach far greater productivity than a replaced workforce. Companies that retrain and re-deploy staff can expand what they serve new markets, new product lines, deeper customer relationships instead of shrinking capacity.

In short: firing people to save costs today can destroy the very capability you need to grow tomorrow.

Lean, reimagined

Consider the Lean analogy: when some organizations use Lean to continually cut costs and offshore work, they can hollow out capabilities. By contrast, companies that truly embraced Lean principles like many Japanese manufacturers, invested in people, training, and continuous improvement, enabling them to successfully expand and even bring production back to new markets.

Agentic AI offers a similar fork in the road. If you only use it to do the same work with fewer people, you might win short-term savings. If you train your teams to master agentic tools, you multiply what your people can achieve: more products, broader services, faster learning.

Uncertainty is real but so is judgment

No one knows the absolute long-term truth about superintelligence or the full scope of disruption. That uncertainty calls for humility, not paralysis. My conviction is simple: place your bet on human potential. Invest in reskilling, build strong governance, and keep humans central to design and oversight.

Practical steps for leaders

• Train first: Upskill teams on agentic tools rather than shrinking headcount immediately.
• Redesign roles: Move people into higher-value jobs that use empathy, judgment, and creativity.
• Adopt guardrails: Implement permission layers, audit logs, and human-in-the-loop checks.
• Measure growth, not just cost: Track new revenue opportunities, products launched, and markets entered.
• Engage stakeholders: Work with unions, communities, and policymakers on transition plans.

A positive, human-centered vision

Agentic AI will change work profoundly. The future we get depends on choices we make today. I believe the best path is one where companies empower people training them, entrusting them with higher-value tasks, and using AI to amplify human creativity and care.

If we build that future thoughtfully, we won’t merely replace effort with automation. We will expand what humans can imagine and build moving out of the box together.

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Coming next: Episode 5 will explores how AI agents talk to each other: coordination, negotiation, shared memory, and the foundations of multi-agent intelligence.

Agentic AI: MCP Model Context Protocol, giving agents access to the real world (Episode 3)

Large Language Models are powerful thinkers, but they have a limitation: they cannot act on the world unless someone manually wires them to tools, apps, or data sources. The Model Context Protocol (MCP) changes that. It provides a universal, open standard that lets any AI model connect to tools safely, consistently, and without custom integrations.

If the LLM is the brain, MCP is the nervous system that links intelligence to real capabilities.

Why MCP matters ?

AI agents need more than reasoning, they need interaction. MCP enables exactly that:

• Real-time tool use (APIs, databases, workflows, productivity apps)
• Structured context shared among tools and agents
• Safe autonomy through explicit permissions and transparent actions
• Interoperability across ecosystems and providers

MCP creates a unified way for models to understand what tools can do, request actions, receive results, and continue reasoning in a loop.

MCP in simple terms

MCP defines how three components communicate:

• The model —> thinks and decides
• The client —> sets goals and instructions
• The server —> exposes tools and actions

The flow is simple: the client exposes available tools → the model decides which action to take → the server executes → the model continues based on feedback. This creates a smooth "goal → action → feedback → adjustment" cycle.

MCP vs Traditional APIs, why MCP is different ?

MCP is often compared to APIs because both allow software to access functionality. But they operate very differently. Here’s a clear perspective:

1. APIs are built for software-to-software communication

APIs expect precise calls, strict schemas, and deterministic behavior. They work perfectly for apps, but not for LLMs that produce flexible, natural language instructions.

2. MCP is designed for model-to-tool interaction

Instead of requiring developers to adapt tools to each model provider, MCP standardizes:

• How tools describe themselves (capabilities, inputs, outputs)
• How models request actions (structured, validated)
• How results are returned (safe and transparent)

3. APIs require instructions; MCP provides context

APIs demand exact calls. MCP prepares context ahead of time, allowing the LLM to reason with a full view of what tools exist and how they can be used.

4. MCP is multi-model, multi-agent, multi-platform

An MCP server works not just with one model, but with any LLM that understands the protocol, enabling:

• agent-to-agent collaboration
• shared memory and shared tools
• consistent safety across platforms

In short: APIs are communication channels. MCP is an integration framework designed specifically for AI.

Benefits and real possibilities

With MCP, an AI agent can:

• query databases and CRMs
• edit documents or spreadsheets
• run automations in Zapier or n8n
• access files and knowledge bases
• collaborate with other agents

This transforms the LLM from “a conversation partner” to “a capable actor” with tools, context, and awareness.

Risks and responsible use

Alongside the opportunities, MCP introduces new responsibilities:

• Over-automation —> agents may take unintended actions
• Data exposure —> tools may reveal sensitive information
• Ambiguous intent —> misunderstood requests can trigger incorrect actions
• Safety drift —> agents may chain actions in unpredictable ways

This is why MCP includes permission layers, tool declarations, structured validation, and human oversight mechanisms.

A new interaction layer for AI

MCP represents a shift from LLMs as isolated text generators to connected, tool-using agents. It is the bridge between intelligence and action, providing the structure needed to build safe, autonomous systems that can truly collaborate with humans.

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Coming next: Episode 4 explores the future of work in the era of agentic AI.

Agentic AI: Understanding the types of "AI Agents" (Episode 2)

Artificial agents didn’t appear fully formed. They evolved slowly, iteratively, and sometimes unexpectedly much like the early stages of human reasoning. Today’s Agentic AI systems, capable of coordinating multiple specialized agents to pursue complex goals collaboratively, are the result of decades of refinement.

If Episode 1 traced the shift from prediction to generation and onward to automation and autonomy, this episode dives into the building blocks of autonomous behavior, the different types of AI agents that form the foundation of today’s intelligent systems.

Each agent type represents a distinct way of “thinking” about the world, from reacting instantly to planning strategically.

1. Simple reflex agents, intelligence as instant reaction

The most primitive form of artificial intelligence. Reflex agents operate like a thermostat: see something → react immediately. They have no memory, no context, and no anticipation. Fast and predictable, but limited when situations become ambiguous or complex.

Strength: Extremely fast and predictable. Limitation: Easily confused by complexity.

2. Model-based agents, when perception meets memory

Model-based agents maintain an internal representation of the world. They remember recent events, infer hidden state, and update their internal model as new data arrives. This ability to hold a model of the environment enables better handling of partially observable situations.

Strength: Can reason about partial observability. Limitation: Still fairly reactive with limited long-term planning.

3. Goal-based agents, intelligence gains direction

Goal-based agents act with purpose. Instead of merely reacting, they evaluate actions by whether those actions bring them closer to a defined objective. These agents can plan, sequence tasks, and weigh alternative paths before acting.

Strength: Capable of planning and sequencing. Limitation: Goals are externally defined and typically not self-generated.

4. Utility-based agents, choosing the best action

Where goal-based agents ask “will this achieve the goal?”, utility-based agents ask “how well will this achieve the goal?” Utility introduces trade-offs, preferences, and optimization into decision-making allowing agents to balance multiple criteria and pick the best outcome.

Strength: Nuanced decision-making and optimization. Limitation: Designing robust utility functions can be difficult.

5. Learning agents, systems that improve themselves

Learning agents adapt from experience. Instead of relying solely on rules or fixed models, they update their strategies based on feedback and outcomes. This learning capability is central to modern agentic architectures that refine behavior continuously.

Strength: Self-improving and versatile. Limitation: Can be hard to control and may amplify biases if not carefully governed.

6. Multi-agent systems, when intelligence becomes collective

The most powerful and complex form: multiple specialized agents collaborate, communicate, and coordinate. Modern Agentic AI often composes orchestrators, planners, memory systems, and role-specific agents that together solve tasks no single agent could handle alone.

Strength: Scales to complex, multi-step problems. Limitation: Coordination, safety, and emergent behaviors become central challenges.

A clear trajectory

When we zoom out, the evolutionary path becomes clear:

• Simple reflex → react instantly
• Model-based → maintain a state
• Goal-based → pursue objectives
• Utility-based → optimize trade-offs
• Learning agents → improve from experience
• Multi-agent systems → collaborate and orchestrate

What started as simple reaction loops has grown into coordinated, memory-driven, goal-oriented networks capable of planning, learning, and cooperating in ways that echo human organizations. This evolution explains why Agentic AI is more than automation: it’s the emergence of structured, collaborative, adaptive intelligence.

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Coming next: Episode 3 will explore how AI agents communicate with external tools and systems through the Model Context Protocol (MCP), a powerful standard that enables truly autonomous, tool-driven intelligence.