As you move up in an organisation’s hierarchy and also in your career, you slowly begin to experience a very strange problem - one that’s not always explicitly felt, or you begin to feel it, but you struggle to come to terms with it when you want to understand, discuss and make crucial decisions - ‘Distance from the Details.’

What does ‘Distance from the Details’ really mean?

‘Distance from the Details’ is the progressive divergence, across every professional’s working life, between their operative mental model and ground-level operational reality, driven by their own organisational elevation or time away from practice or doing things. In short, hierarchy clouds or hides professionals from operational reality.

How do you, as a professional, get into this loop?

As a professional, you go through three phases in your working life.

In the first phase- let’s call it the Immersion years - it’s your early professional years, which are 0-10 years of your working life. You stay close to opportunities or problems and address them head-on. This is a phase where you do things yourself. Any error surfaces quickly, and you fix it because the feedback loop is tight and your reporting manager closely manages or corrects you.

To improve your expertise, research suggests three interventions-repetition, immediate feedback, and iterative correction of specific weaknesses. You get all three involuntarily at this stage of your career.

In the second phase - let’s call it the Direction Years - it’s your middle professional years, which are 10-20 years of your working life. This is when promotion arrives in your life, moving you from ‘doing to directing.’ Every promotion during these years pushes you into a role where you no longer do the work but instead describe and supervise the work getting done. You slowly begin operating on a false model of the work, without feeling the impact it creates in your career. In this phase, information distortion slowly begins. You no longer have the operational calibration to detect the gap between the filtered summary notes and the reality.

Research on ‘skill fading’ around clinical skills, which can also be applied to technical or functional skills, found that ‘skills decline measurably over periods as short as 6 months of non-practice, following a decay curve steepest at the outset.’

In the third phase - let’s call it the conceptualization years - it’s your top professional years, which are 20+ years of your working life. By now, you're far removed from the details. You've moved from handling raw data to receiving curated summaries. As a senior leader, you are now operating almost entirely on pattern recognition from a professional context and role. As a senior leader, you begin to operate on a 15–20-year time horizon, while the frontline employee operates on a time horizon of days to weeks. These are different kinds of realities applied at totally different time scales.

In this phase, many senior leaders think they know how the operation runs because they ran it once. However, they don't know how it runs now, under current conditions and constraints.

Research shows that if you stop practising a cognitive, accuracy-based skill, you will have lost approximately 50% of your proficiency within 48 days of non-use. Research further shows that for physical, speed-based skills, you will lose 50% of your proficiency after roughly 106 days without practice. Research shows that, compared to cognitive accuracy skills (48 days), the body forgets more slowly than the mind. That is why you can still ride a bicycle after ten years without riding one, but you cannot accurately value a company after ten years without continuously doing financial analysis.

Therefore, your ‘Distance from the Details’ progressively affects your ability to see opportunities and problems, identify blind spots and make informed decisions in line with reality. A senior leader who stays close enough to the ground will have an enormous advantage over one who relies on sanitised, filtered summaries.

How ‘Distance from the Details’ can affect professionals.

• Dennis Muilenburg, CEO, Boeing: Dennis was CEO from 2015 to 2019, and though he was an engineer by training, he spent much of his senior years in program management, contracts, and investor relations. By the time he became CEO in 2015, he was managing Boeing's narrative rather than its engineering. It was during his tenure that the MCAS system, the automated stall-prevention software that drove two 737 MAX aircraft into the ground, killing 346 people, was approved without its risks being adequately surfaced to the CEO's level. Muilenburg had assured the public that the 737 MAX was "as safe as any airplane that has ever flown the skies." He may not have been aware of the engineering, or may not have known, that statement was false. Muilenburg was unfortunately fired in December 2019. Here’s an example of a technically trained leader who rose through the hierarchy until his engineering intuition drifted so far from current engineering reality that he couldn't see what his organisation was missing.

• Howard Stringer, President and CEO, Sony: Howard Stringer led the company from 2005–2012, and he was appointed to restructure a hardware-heavy organisation for the content era. Stringer backed managers who would execute his decisions over engineers who could deliver innovation. However, Sony's competitive advantage had always been in its engineering culture - "Sony engineers generated an average of four new product ideas every day." Because of his professional background in broadcasting, not product engineering, Stringer failed to sense this detail. He was distant from its technical identity. He wasn't distant from Sony's financial reality.

• Rana Kapoor, Founder & CEO of Yes Bank: Rana Kapoor led Yes Bank from its inception in 2004 until 2019. He built Yes Bank as a part of India’s growth story in the 2000s with aggressive lending to corporate borrowers that other banks considered too risky. The loan portfolio grew rapidly. But the credit quality underneath was deteriorating. However, the official numbers were systematically disconnected from the operational reality of borrowers’ health. This created a liquidity crisis in the bank’s operations, which led SBI to take over the bank. Rana Kapoor's operating model of his loan portfolio did not match the reality on the ground. Either he chose not to know, or the internal reporting systems were designed to keep the real picture from surfacing.

So, how do you overcome ‘Distance from the Details’ syndrome?

Here are some ways in which you can overcome this problem that organizational structure forcibly takes you away from the details:

1. Practice deliberate decent: As a professional, it may be important to understand and acknowledge that supervising work is not the same as doing the work. Regardless of your hierarchy in the company, find ways to structure and engage in operational details, like personally handling a client, building a model, or writing a proposal.

2. Carve-out practice time: It’s always hard to find time to ‘do’ things yourself, as work will always be hectic and there will always be time pressure to get things done. Carve out 10–20% of your time for practice of the core craft that strengthens your functional skills, not just management of the craft or review of the craft, but remain in the execution of it.

3. Find ways to bypass the organisation hierarchy: Try to maintain direct access to people at the operational level who are not in your reporting chain and who have no incentive to filter what they tell you. Build a nuanced method or regular cadence with them to make it a process without affecting your direct reports. These are ‘sensing the environment’ meetings for you and not ‘fixing the problem’ meetings.

4. Build a reverse mentorship structure: Invite junior professionals whom you work with to share their perspective on your decisions and current operational reality. Don’t be defensive about the feedback. Treat it as a structured intelligence-gathering mechanism. Your junior shares the current operational issues, while you, as a senior, can spot patterns and interpret what they're seeing. Combining both can be more accurate than either alone.

5. Have a beginner’s mindset: As a senior professional, your biggest trap is the certainty mindset you jump into, feeling like you have seen and solved this problem before. With experience, the number of possible solutions shrinks. Without a beginner’s mindset, you will only know what you think you know. Approach every operational problem as a genuine learner and address it as if you are trying to understand and do it for the first time.

As you move up in any organisation’s hierarchy, your professional growth and success depend not only on your ability to think strategically but also on how well you stay connected to the ground, while balancing that with a deeper understanding of operational reality. It’s all about finding ways to reduce your distance from the details.

A couple of months ago, I wrote about curiosity culture and the importance of being a curious professional. This is becoming increasingly important in the age of AI and the change that is happening all around us today.

One thought that kept coming up was the point that while everybody understands the benefits of being curious, why is it so hard to practice it?

More importantly, do you recognise what it will cost you personally and professionally to practice curiosity?

Not enough is discussed or written about the challenges and implications you would face if you want to stay curious or be a ‘curious professional’.

When you want to be curious or behave like a curious professional in an organisation, here are the challenges:

• Curiosity is a weapon that can work in the exact opposite way it was originally intended. When you question a belief, the people involved don't experience intellectual challenge, they experience a personal attack. You will be perceived as aggressive.

• Research findings published in an HBR article found that curious employees annoyed managers! Managers saw them as insubordinate or less likeable. If a manager couldn't answer a question, they lost status in public, so they tended to suppress the curious employee who, by nature, kept asking questions.

• Curiosity, which is a mechanism for asking inquisitive questions, is something any hierarchical company structure tends to suppress. It creates information asymmetry, which leads to power asymmetry within a company’s hierarchy, as the person closest to a problem knows it best but has the least leverage to ask questions.

• Companies often don’t promote the person who asks the best questions, as they're seen as someone with a lot of doubts. They tend to promote the person who shows a ‘sense of certainty’ about what has been assigned to them.

• People also face ‘conformity pressure’ and this normally restricts their ability to stay curious.

The Curiosity Paradox - A case of misaligned expectations

Companies, knowingly or unknowingly, punish curiosity!

Ask any leader if curiosity is important they will vigorously nod in agreement, but they will continue to reward compliance.

• Curiosity Paradox #1: Research shows that 95% of workers say they're naturally curious. But only 30% report that their workplace strongly rewards curiosity.

• Curiosity Paradox #2: Another study found that 44% of workers say asking too many questions makes them look incompetent.

• Curiosity Paradox #3: Nearly half of Gen Z workers (42%) stay silent because they feel they've already asked too many questions.

Here’s what people had to go through for sticking to their curiosity instinct that ran quite contrary to widely held theory or beliefs:

• Quality guru Edwards Deming, who believed in total quality management, was once a consultant without believers. American manufacturers rejected his ideas, and he then went to Japan, where Japanese companies adopted his quality-management thinking. He was curious about quality as a system, and he challenged the entire management philosophy of command-and-control manufacturing. Deming was finally brought back to America at the age of 80 to teach Ford what it had refused to learn from him 30 years earlier!

• Ray Dalio, an American investor, was fired in 1974 for being publicly right about a market call in a way that embarrassed his superiors. His curiosity about macroeconomic first principles produced conclusions that contradicted institutional consensus.

To remain curious, be aware of the downsides of curiosity.

Staying curious certainly requires courage.

It requires ‘intellectual courage’ which includes the willingness to challenge authority based on data and evidence, to be fine with being publicly wrong, to keep sustaining inquisitiveness when the environment around still punishes it, and to have the mental stamina to keep asking open questions longer than any system's patience allows.

Most work environments allow only for ‘pseudo-curiosity’, and this can often mislead you - environments that never challenge anything important, raise questions that make you stay safe, and this gives you a dubious feeling of being curious while producing the same answers as everyone else.

The question is not ‘Is curiosity worth it?’ However, the question is ‘What is the honest price, and are you willing to pay it?’

To stay curious, it pays to appreciate the downsides of curiosity rather than focus on the bright side and its benefits.

1. Are you ready for a slower rewards timeframe? Curiosity takes time. Deep inquiry is slow. Most organisations reward velocity over depth of thinking and execution, which takes much more time. Because reward systems aren't aligned with this structure, you will be forced to take shortcuts to show quick results and earn rewards. The curious are willing to accept a relatively late reward cycle. Ask yourself, “Am I willing to wait for my rewards far more slowly than others but committed to staying curious, even if there are no immediate rewards?”

2. Can you resist short-term approval? The ‘conformity pressure’ stress-tests your conviction. Do you sacrifice your ‘deeper gut feeling’ for want of short-term approval in a group when certain plans, topics, or strategies are discussed? You must be willing to be unpopular before being vindicated, and you must also be ready to accept that vindication is not guaranteed.

3. Are you willing to break the comfort of consensus? Staying curious has a social cost. You are forced to remain in a ‘zone of consensus ’, which is the conformity trap you can get into. Every team or organisation you work for normally runs on an informal agreement that certain questions will not be asked. Ask yourself - “How ready are you to break the unspoken contract?”

4. Are you ready to overcome setbacks that could pull you down? If you practice curiosity diligently, you may face career setbacks because organisational incentive systems are misaligned, you may risk being excluded from established leadership tracks, or you may be repeatedly passed over in organisations that promote compliance. You need the stomach to handle such setbacks, which may set you back for a couple of years but could eventually help you catch up with others.

To stay curious and experience trust, acceptance, achievement or success, timelines can be brutal. You have to build the muscle to survive in the interim period.

At workplaces, it is not rare to hear people lament, “We have very few people who can think out-of-the-box”. Most startup founders, business leaders, promoters, and business owners will tell you how many top performers from large enterprises struggled to transition to a startup or a new business-model environment. The reason is simple. Most people tend to manage and help scale what is already built, but very few can innovate by first thinking out-of-the-box about any problem and then scaling the idea or the process.

Scale was the moat. It no longer is.

Progressively, scale, which was a source of power, is getting dismantled. For more than 200 years, competitive advantage was simple - do more of the same thing, faster and cheaper. The business owner who could build 10,000 units where others built 1,000 owned the marketplace. The bank with 10,000 branches beat the one with 100. This model is being disrupted because AI is commoditizing execution. Therefore, throwing more people at the problem is no longer a competitive advantage, as agents can do it better and faster, and over time, network effects will begin to plateau.

We are therefore entering a future where execution is infinitely scalable and nearly free, and the only appreciating asset will be the capacity to think - thoughts that haven't been thought before.

Education was the moat. It no longer is.

Let’s look at this moment through a different lens.

Let’s first analyze what our formal education system prepares our people for. The formal education methodologies and the current education ecosystems were primarily designed to do two things:

1. Transmit existing knowledge at scale

2. Produce standardized worker/employee types

How does it transmit existing knowledge at scale?

Formal education does this through standardized curricula, graded assessments, sequential knowledge acquisition, and standardized testing and measurement. It discourages the generation of novel thoughts and ideas. The current education model was not designed for learning. It was designed for industrial compliance.

In a landmark study, Dr. George Land created a divergent thinking test for NASA to identify innovative engineers. This was administered to 1,600 children at three points in their school lives:

• At the age of 5, 98% scored at the “genius” level for divergent thinking

• At the age of 10, only 30% scored at the “genius” level for divergent thinking

• At the age of 15, 12% scored at the “genius” level for divergent thinking

• When administered to 280,000 adults, only 2% scored at the “genius” level for divergent thinking!

The only variable that changed between age 5 and adulthood was formal schooling! And the only variable that declined was creative thinking capacity.

The grade system, which is the operating measurement method of formal education, has become a ‘suppression engine’ for the motivation that drives original thought. According to Clayton Christensen, the late legendary Harvard Business Professor, “the most educated, best-trained people in an industry - those who understand it most deeply- are precisely the ones who fail to see disruptive threats.”

Formal education develops delivery skills like analysis, planning, attention to detail, and disciplined execution. These are execution capabilities. Formal education is hence structured to create more of the same people with pretty similar skills - who can get things done in a similar manner. Companies desperate to scale and win sought them out in the past. That is being disrupted.

How does it produce standardized workers/employee types?

Standardized workers/employees are those people who predominantly apply rules, follow decision hierarchies, process and manage transaction workflows, and produce and deliver predictable outputs. Give them a new problem, and they struggle. But when you ask them to do more of the same across functions and geographies, they simply excel at it. A structural shift is underway in how ventures are formed or will be restructured. Recent data on solo and micro-enterprise formation is just incredible and mind-boggling. Solo founders as a share of new startups jumped from 23.7% (2019) to 36.3% (mid-2025), a 53% increase in six years. The minimum viable enterprise size is dropping sharply.

Therefore, machines and general-purpose technologies like AI can scale any codified process infinitely at near-zero marginal cost. The only non-commoditizable resource left is the ‘thought’ that creates new frames, not the execution within existing ones. If you're working or a student, you are witnessing one such moment in history. Many more will come over the next 100 years. These are moments when standardization and scale begin to break down. You therefore need to adapt to new ways to learn, think, and work as enterprise sizes shrink and tighten over the next few decades before they start scaling up again.

It’s increasingly all about frame-breaking, not frame-optimizing

Most systems in the past - be it workplaces or education- rewarded you for operating within the box, executing by the rules, pre-defined measurement and scoring parameters, standard remuneration and promotions, and SOPs (Standard Operating Procedures) as envisaged by the people who set this up. They had workflows and processes to improve by doing the same thing faster - create more of the same, more efficiently. By their own definitions and boundaries, their ceiling became the edge of an existing frame of reference.

What you will increasingly need is out-of-the-box thinking and action.

Out-of-the-box thinking is the capacity to see the box that exists, question why it's shaped that way, and propose a different box entirely. The best analogy to bring this to life is comparing a chess grandmaster and a chess engine. Both are “thinking” about chess. The only difference is that the engine calculates 40 moves ahead, which is pure scaling of ‘within-frame’ logic. But the great grandmasters who invented modern chess didn’t play better chess, they redefined what good chess was. That is out-of-the-box - ‘outside-frame’ logic.

Once a new frame is created, thousands scale it for decades.

In the 1920s, you needed both the idea and the factory. In 2026, you need the idea, and the factory (execution layer) is available on demand. The idea need not be limited to a new startup, but it can be work and processes within your function, even in a large enterprise. What took many people to do can be done by a few.

Therefore this requires

• Skill agility: It requires the ability to learn and unlearn faster than the pace of external changes happening around you. This isn't about known situations that have rewarded people over the last 75 years: “earn a degree”, “Azure or AWS certified”, “learn Six Sigma”, “learn AutoCAD,” "learn Python", “learn Java”. Now, agents can do these at scale. Thinking about what you can do or what was not done with these tools will become more important. It’s about learning how to learn the next thing that hasn't been invented yet.

• Divergent Thinking: Becoming uncomfortable with old and known ways of doing things. Be ready to ask and work with people who are willing to say, “Here is what we think we know, question whether the framing of the problem is right.” Be ready to interrogate the reality being given, discussed, presented, or shared with you.

• Know more domains: Don’t see yourself as only a software developer, sales analyst, procurement or finance specialist. You cannot make analogies if you know only one domain. A pattern from biology could apply to organizational design, or a principle from architecture could apply to business strategy. This is the primary thought mechanism for generating non-standardized insight and work culture.

• Move from problem-solving to problem-finding: Problem solvers are overhyped. They are valuable when problems or established ways of doing things are known. Maybe what is needed is to look at the problem through a new lens. It is worth taking inspiration from what Einstein said: "If I had an hour to solve a problem, I'd spend 55 minutes thinking about the problem and 5 minutes thinking about solutions." Become a problem finder.

• Build demonstrable work, don’t accumulate just credentials: Any degree or certification only certifies that you sat through the curriculum and cracked some tests. However, any portfolio that you create proves how you think and build. A portfolio is nothing but projects you have done, products you've tried to build, problems you've found and solved, and your published thinking.

You are moving into a transitionary phase where skills and scale are being redefined. You need to think and act out-of-the-box to get prepared for this change. You can only make this transition effectively if you identify and develop capabilities the previous era did not require.

“How Does Someone Entering The Workforce Now Build Real Expertise?”

This was a question posed by a reader of The ContraMind Code newsletter.

It was an interesting question in response to an article written a few weeks ago titled “Are You Building the Right Expertise?”

This is an extremely relevant and difficult question to answer. Because a young professional just entering the workforce doesn't have exposure to handling many similar or near-similar problems as they arise, nor do they have years of accumulated capability built through work experience under their belt. So how do they enter the workforce today with any proof of expertise to begin with and build it further?

Increasingly, entry-level skills demand senior-level skills

In a 2026 study, PwC found that the new tasks added to AI-exposed roles are 2.5 times more likely to rely on skills such as empathy, judgement, and creativity, which become even more valuable as AI absorbs some routine work. PwC calls this trend the "seniorization" of entry-level work. The study found that AI-exposed junior roles are seven times more likely to demand traditionally senior skills such as leadership and strategic thinking (compared to the least exposed junior roles). AI is ‘professionalising’ some jobs by reshaping them to require even more human expertise. AI ‘democratises’ other jobs by making them even easier for non-experts to perform.

The challenge is even greater for middle-level professionals, too, as they must change how they work and reskill faster than ever.

So, what does it take to build senior-level skills?

First things first, senior levels do not mean just designation-led positions, but rather senior-level skills that require deep customer knowledge, industry context, problem-solving, critical thinking, handling ambiguity, judgement, leadership, etc. This means it no longer matters which level you are in an organisation. In a consulting firm, you require many of the senior-level skills mentioned above right at the entry-level, and this climbs up in ‘degree of capability’ as you grow into the middle-level and senior level there. The same may not be true in every industry, but levels are crashing, crumbling, and flattening by the day. What was mid-level or senior-level skill yesterday is today’s entry-level skill!

Building these senior-level skills? Break down existing mental barriers

• Removing the widely-held belief that entry-level work is the destination and work experience will take care of itself. It is just the vehicle you have got into for your long journey called career. Experience is built when there is ‘deliberate struggle’ to first do something, struggle by making mistakes, and that’s how knowledge accumulates. When it feels easy, you may almost always be accumulating years of work, not years of deep-work capability. AI tools make a task feel effortless but are a poor predictor of improving your expertise. It needs more ‘deliberate struggle’ to take the effort to do it first and then validate it through AI.

• A study by Lisanne Bainbridge, a leading cognitive psychologist, found that automation led to a loss of manual judgement during emergencies! Remember, the machine doesn’t replace your skill, it lets your skill slowly expire.

• Don’t resist correction or rework till done right. The first presentation you did, or the first-draft analysis you prepared, or the code review that happened- none of it is a standalone or independent task that can be outsourced. It made a difference because a senior or an experienced peer corrected it, and that’s where your learning began. Keep a count of the corrections you make on anything you do until you get it right.

• If you have to build senior-level skills like problem-solving, judgement, etc., you do so by ‘doing hard things’ badly, in front of someone who tells you exactly why.

  • A surgeon does not become an expert overnight. They do the hard things under the oversight of senior surgeons. A trainee surgeon "must have observed or assisted" surgeries during their PG programs. They do hundreds of operations before they are called a chief surgeon. The ‘ladder of autonomy’ is slowly opened up for them, not in months but over decades. They begin as residents/assistants and junior surgeons, then become chief surgeons. Every observation, every assistance they provide during surgery, and every surgery they perform under supervision helps them build their expertise.

  • In a landmark 1993 study, Anders Ericsson, a well-known Swedish psychologist, studied the difference between elite and good violinists and found that elite violinists had accumulated 7,410 hours of ‘structured, coached, solo practice’ with corrective feedback by age 18, compared with 5,301 hours for good violinists. The key point is ‘structured and coached’ practice, not just raw practice.

• It is vital not to confuse experience with expertise. There are many seniors with experience who have failed to develop their expertise despite the opportunities available to them. In contrast, some youngsters, through sheer intensity and effort, have built significant expertise but lack experience. Find the right senior or a peer who can teach and help you grow. There may be a select few around.

Prepare yourself for ‘Deliberate-Supervised-Struggle’

1. Embrace ‘deliberate practice’: Designations solve the need for recognition, not expertise. When you follow the art of deliberate practice, it allows you to define goals just beyond your current ability. You seldom stop at what you know, instead, you focus on what you don’t know, what you are not good at, and what you need to get better at. If you look at professions like surgeons, architects, chartered accountants, musicians and lawyers, none of them says “get some experience.” Surgeons require a certain number of ‘surgery hours’ to be completed to become surgeons. The same is true for architects, CAs, etc. Vague experience is unauditable. All of them go through a ‘ladder of autonomy’ as they grow in stature, but it is closely linked to the expertise that they have built over time. Pick the areas of skills or expertise where you want to put in the ‘practice hours’ beyond ‘work hours’, but seek structured supervision. Without that, deliberate practice is of no consequence in your path to building expertise.

2. Seek ‘Structured Supervision’: Like the findings of the elite violinist research, seek out people who are good at what they do - it can be either your seniors or peers, or people outside your industry who can help you with structured supervisory inputs. They are the ones who have the tacit knowledge. They could be somebody who can challenge your assumptions or your output, direct you to the right sources of information that can address your queries, bring forth their experience having been in similar situations or solved similar problems, recommend simple tweaks that may help you do things faster, or improvise on what you have built or recommended, etc. When you struggle without conditions that can reliably help or direct you, it can simply produce confusion and frustration. Every struggle must be followed by expert correction. An internship, an apprenticeship, or talking to a reporting senior alone does not build expertise. It requires someone who can observe and correct, explain your thinking more clearly, give you space to make mistakes while protecting you from adverse consequences, and help you articulate your thoughts more clearly.

3. Factor for ‘Struggle Time’: Your study grades or new tool certifications and daily work deliverables focus on the here and now. It is either an immediate assignment, an upcoming exam, a client request, or a task deadline. Cracking this on time only requires planning and discipline. AI tools may help you compress the time to get them done but remove the process of struggle, which is critical to building expertise over time. Therefore, factor in ‘struggle time’. Struggle improves your memory’s storage strength (how durably something gets embedded) and retrieval strength (how easily it can be accessed right now). Struggle leads to stronger long-term learning, improving muscle memory and, in turn, building your expertise. Don’t simply reach for the AI tool when a problem is posed to you with a deadline. Spend time working and curating after AI throws answers at you. Interpretation is a struggle too and not easy.

Most companies only attempt to take you through a ‘title ladder’ but skip the question of mechanism and the motivation entirely and therefore are rarely conducive to developing your expertise. Building expertise lies solely in your hands.

To summarise, if you are entering the workforce, to build expertise in the profession you have chosen and in the work that you do every day, personally build a sense of choice in your work routines, a sense of effectiveness to get visibly better at something, and a sense of meaningful connection with others at work for a shared learning culture.

When people get a 4.0/4.0 GPA or a relatively high GPA after cracking a tough competitive entrance exam, or after getting a hard-fought admission to a top-tier university or college from among thousands of applicants, the most important assumption they make is that their upward career trajectory is pretty much set for life.

It may be worthwhile to pause and ask yourself: “Like for others, is that enough for me to be successful and will it really help me grow in my job or career?”

Let’s put the tsunami of degrees in context

Today, the global annual degree output is approximately 50–60 million!

If you look at this growth over the last 45-year period, it went from approximately 15 million/year to approximately 55 million/year, that’s a growth of roughly 30–35 million/year.

Which countries contribute to this global growth in degrees? And which countries lead the list?

• China: Approx. 10–11 million/year (it grew from 1 million in 2000)

• India: Approx. 7–8 million/year

• US: Approx. 4–5 million/year (all levels)

• EU: Approx. 4 million/year

• Rest of the world: Approx. 15–20 million/year

You may be surprised to learn that the last 45 years (1980–2026) have produced approximately 80–85% of every degree ever conferred in human history! We live in an era of credential and grade inflation!

Therefore, degrees are no longer scarce assets. There has never been a weaker differentiating signal for degrees than it is right now in human history! Degrees have become baseline qualifications, the minimum for entry, and no longer stand as markers of distinction. It’s a fact that there has been a progressive decline in a degree's role as a differentiating credential, and it carries an increasingly low credential reputation compared to many decades ago.

It’s not that degrees are worthless, but we live in a time when, globally, 50–60 million degrees are issued annually. The genuine differentiator that will sit atop any degree is only character.

So, what’s character?

This is perhaps the most frequently misused word!

Let’s first try to unpack what character is not. Character is not personality. It is not talent. It is not reputation, which is nothing more than what others think of you. It's not just about honesty, trustworthiness, and the like.

Character is the consistent pattern of how you respond to the world, especially under pressure, in private, and when no one is watching. Character is:

• Alignment between your stated values and your demonstrated behaviour

• Ability to hold a principled position under pressure

• Accurate self-knowledge and willingness to say "I don't know"

• Learning orientation irrespective of experience and seniority

Character is the result of habitual action, reinforced until it becomes a disposition or a default habit.

Character, at its core, is the reliable alignment between what you value, what you decide, and what you do — especially when alignment is costly.

How did degree systems replace character?

The modern university degree is no more than 900 years old. The degree system began measuring only information acquisition, technical skill, and one’s ability to perform under exam conditions. However, it did not measure alignment between values and actions under pressure.

It may be very insightful to understand how capable people with expertise were recognised before degrees ever existed!

In fact, research reveals that the degree system slowly displaced character from the centre of professional recognition. By 1900, character education, which had been at the centre of higher education for hundreds of years, had largely disappeared from students' curricula. The universities reorganised themselves around knowledge production rather than character formation.

Credentials can, at best, open doors. However, character determines what you do once you are inside. This is the primary reason you see many people disengaged in the workplace. Or change career paths or occupations when it no longer aligns with what they value and what they want to do.

So, when does character become more important than your degree?

By year five!

A degree gets you an interview when you are 22. But by the time you are 35 or 40, no one asks about it. According to research, a degree shows a low correlation with your performance by the fifth year. Because by that time, your actual track record - your reliability, how you handle failure, whether you make the people around you better gets replaced as the primary signal, as opposed to your degree as a credential.

Just think for a minute:

• No one asks for your degree before entrusting you with a company's sensitive strategy!

• When you are co-opted into confidential projects or called to lead and diffuse crisis situations, nobody is asking for your degree.

Why?

Because your reputation travels faster than your resume.

Also, your degree does not update itself. However, your character - the discipline to keep learning, taking calculated risks, the ability to take ownership, the honesty to acknowledge what you no longer know, and the courage to adapt keeps getting updated. Because character is the only credential that remains current all through your life and career.

How do you go about building your character?

If research shows that, after a certain number of years at work, character starts to take precedence over your degree, questions that will be on top of your mind are:

• “Is character inborn or can it be built?”

• “Is it possible to keep improving my character by working on it continuously year after year?”

The answer is a simple yes.

Research shows that character change happens through specific behavioural practices in real conditions with real feedback. The mechanism is termed habituation - ‘the repeated performance of virtuous action until it becomes automatic.’ Don’t confuse this with willpower. Willpower is a finite resource. Through repetition, you transform a character you want to build (which once took a lot of effort for you) into something that will eventually become natural. Remember, any form of growth requires friction, feedback, and sustained engagement with genuine difficulty.

What are the variables that can predict your upward character trajectory?

• Learning orientation over performance orientation: Treat challenges as opportunities for development rather than threats to your competence or reputation.

• Develop your metacognitive capacity: Metacognitive capacity is the ability to think accurately about your own thinking. Your appraisals and promotions don’t always tell you the truth. It is only a reward for a job you were given. You must develop a genuine sense of being wrong and build systems to recognise it faster. It’s like answering this question - “How do I know when I am wrong about something important?". When you have poor metacognitive capacity, you struggle to find answers to this question, treating it more like a process and outcome, which is not always true.

• Improve your relationship with feedback: Again, ‘being open to feedback’ is a frequently used sentence at work. Character gets built beneath this sentence. It’s answering a question like - “Would I be able to tell a time when feedback changed something meaningful in how I work, think, or relate?” It’s the presence of a genuine story and visible evidence of change.

• Keep calibrating your self-awareness: There is a very big difference between knowing your weaknesses and having accurate self-awareness. When you describe yourself to others and then compare notes about your description versus what others describe about you, that will show you the gap. This is called the calibration error. It’s about knowing every theory in the world about collaboration versus your ability to actually collaborate in reality. And do the people you work with genuinely believe that you are a collaborator? Knowing and working on that gap is tough but valuable.

• Integrate difficulty with your growth: It’s not enough to learn from genuine adversity that you may have faced, but try to intertwine it tightly with your own growth goals by saying, "this experience is part of what made me who I am" without bitterness or false gratitude.

To summarise, character is the accumulated output of thousands of small choices you make consistently over time.

Your degree is a mere entry pass and, in some cases, does not allow you entry, either. However, your character helps you not only get entry but also gain membership in an elite club of achievers.

When building expertise, you need to understand the difference between ‘knowing how to think about something’ and ‘knowing how to do it ’. And you need both kinds of expertise.

For most of your professional life, you are told your depth is your greatest asset. And your breadth of knowledge is not as important, and it is only optional. Expertise is largely seen through a vertical lens. The deeper your domain, the higher your value. The more you know about your function — whether you are in R&D, procurement, software, accounting, engineering, marketing, law, technology, customer service or strategy — the more indispensable you become. You are rewarded for one thing - becoming very good at something specific.

That model served us well for over a century. But it is always built on a hidden assumption that the environment you are trained for would remain largely the same as the one you would work in. During stable times, that assumption holds true. But in a largely disruptive time that we are going through right now, you may need to rethink this.

Before you spend another year building expertise, it is worth understanding what kind of expertise you are actually building — and which type of expertise is now under the most pressure and will be in greater need.

Two types of expertise. One is under threat. The other depends on it.

Harry Collins and Robert Evans, Professors at Cardiff University, spent decades researching and studying how expertise actually works, not how we assume it does.

They identified two fundamentally different types of expertise.

Contributory expertise is the ability to ‘do’ the work. The surgeon who performs the operation, the engineer who designs and builds the structure, the analyst who builds the financial model, the programmer who writes the code. You develop the ability to produce new output in your domain. You possess the ability to contribute to it. If you can do all this, you earn the right to be called a practitioner.

Interactional expertise is the ability to ‘engage meaningfully’ with the work and possess the ability to engage with people who do it without being able to do it yourself. You understand the landscape. You can ask the right questions. You can evaluate the quality of the output within the domain. You can switch and apply concepts between domains, spot when something is not right, and communicate across boundaries. But you cannot do them yourself.

Most of us as professionals sit somewhere on the continuum between these two types of expertise. The question is, when disruptive forces hit, or new technologies shake up industries or the work that you do, you are forced to ask yourself: “What exactly am I doing, and am I still valuable? And is that where I need to be, or what must I change to stay valuable?”

How expertise gets rebalanced and revalued in every era.

In the industrial era, the dominant currency of expertise that professionals had to build was contributory expertise, and of a narrow kind - extremely specialised in the nature of the work they were involved in. Interactional expertise remained a dominant currency of expertise for management roles, say, roughly 15 to 20 per cent of the workforce. The other 80 to 85 per cent were focussed on developing contributory tasks, many of which were narrowed deliberately.

This balance of expertise shifted when the transition happened to the internet and knowledge era. The famous ‘T-shaped professional’ became the dominant professional archetype - deep in one domain (contributory expertise), broad across others (interactional expertise). And the most valued among them were those professionals who could bridge between domains.

David Autor, a professor and economist at MIT who has tracked labour market shifts for two decades, defined a framework for understanding which expertise was safe and which was not. According to him, the key variable was not skill level. It was whether the task was routine or non-routine. The knowledge era did not replace contributory expertise but instead stratified it in a very different way. Routine cognitive tasks (let’s call it routine contributory expertise) in the middle of the market, such as bookkeeping, data entry, structured analysis, clerical reporting, and rule-based decision-making, could be decomposed into steps and automated. This got squeezed and hollowed out. Non-routine cognitive tasks (let’s call them non-routine contributory expertise), such as complex problem-solving, cross-domain judgment, and nuanced communication, could not be replaced. Therefore, deep domain expertise remained valuable. However, the most valued professionals were those who could bridge between domains - the technologist who understood business strategy, the designer who understood systems engineering, the marketer who understood data, etc. The bottom, which included non-routine manual tasks like janitorial work, construction, home care, etc., that required physical presence in unpredictable environments, proved difficult and expensive to automate, and these survived.

How will expertise get rebalanced and reshaped in the AI era?

AI tools are absorbing contributory expertise at a genuinely quick pace. Not physical tasks, but cognitive ones. Drafting, analysis, code generation, financial modelling, legal research, diagnostic reasoning, design ideation, etc. - these were the domains of knowledge workers with deep contributory expertise. AI can now perform versions of these tasks at a level that is, for many applications, good enough. And it is improving rapidly at breakneck speed.

This has therefore led to a widespread belief that interactional expertise is the future. These include interactional expertise tasks such as knowing how to use AI, how to communicate with it (prompt engineering), and how to synthesise outputs across domains (supervisory engineering), which are becoming premium capabilities. ‘The contributor’ is being replaced by 'The interactor'.

This assumption is only half right. The half that is wrong is the dangerous part that labour markets, companies, and, more importantly, professionals have failed to understand its implications and impact.

It’s easy to fall into the hollow expertise trap.

One of the limiting conditions of Prof. Harry Collins’s framework is that being genuinely good at interactional expertise requires some contributory expertise grounding in the domain. You, as a professional, cannot acquire expertise just by asking the AI tool or by reading through the answers that the AI tools generate.

The problem of ‘interactional incompetence’.

Without sufficient grounding in contributory expertise, what looks like interactional expertise leads to what Prof. Harry Collins defines as ‘interactional incompetence’. You can develop vocabulary fluency without an in-depth understanding of the underlying concepts. You can develop the ability to speak and generate the right-sounding words. However, you cannot evaluate whether what you are saying is correct.

A practitioner of medicine, R&D, manufacturing, finance, marketing, procurement, sales, etc. acquires contributory expertise through years of sustained practice and engagement with other practitioners in their field, by attending conferences, debating with other fellow practitioners, reading literature and articles, learning from their mistakes when they applied their own intuition or reasoning, etc. This allows them to spot a flaw in methodology or approach or identify a claim when it is overreaching.

This is the trap that millions of professionals with AI tools in their hands can fall into. Imagine a person who has never built a financial model can now produce one using AI, or a person who has never written a line of code can now produce functional software, or a person who has never drafted a legal brief can now produce a document that looks like one. The output looks credible, but the person’s ability to evaluate it critically and to catch what the AI has got wrong is very hard.

A great conductor has the knowledge of playing many instruments.

Your expertise must be like that of a conductor. You must transition yourself into a conductor who cannot play every instrument but has played several well enough to know what each sounds like when played badly. You cannot be a conductor if you have never played an instrument. Therefore, it is impossible for you to hear or identify what the ensemble is getting wrong. Also, a conductor who has played only the violin cannot hear all that the orchestra is capable of. That’s the contributory expertise built over years that helps a conductor direct the orchestra very well.

You must be an orchestrator with genuine, deep, multi-domain contributory grounding to do what neither the pure specialist nor the pure generalist can. You must be able to produce coherent, integrated output that is greater than any single contributing part.

If you want to be amongst the most effective AI-era professionals, here is what you must build. It’s not enough to have just interactional expertise, which is the ability to speak fluently across many domains. Or build only contributory expertise in one narrow domain. You must build a strong foundation of real, contributory expertise in at least your core domain, and sufficient interactional expertise to operate across adjacent domains. This will give you the ability to direct AI systems with enough evaluative judgment to know when the output is right, when it is subtly wrong, and when it is absolutely catastrophic.

What kind of expertise will the next fifty years require?

Charlie Munger said: “Learning from history is a form of leverage.”

When we look at history, past historical patterns are consistent. With every major technological transition, it initially feels as if it will destroy expertise built in the past, but eventually it creates demand for higher-order expertise that the previous systems could not produce.

• The printing press did not eliminate the need for literate people. It created a world of more sophisticated, mass-distributed literacy than before, but scribes-led expertise became obsolete.

• Electricity did not eliminate the need for people with contributory expertise in mechanical systems. It created a completely new form of contributory expertise in electrical engineering and electronics as an adjacent frontier.

• The internet did not eliminate the need for people with industry-vertical-led domain expertise. It created a premium on people who could connect across domains at a speed the pre-internet world did not require.

AI will follow the same pattern.

1. The contributory expertise which will be at greatest risk is the cognitive tasks that are codifiable. Routine cognitive tasks that can be automated as rules, patterns, and procedures.

2. The contributory expertise that will grow in value will be a lot more tacit, like

   1. Ones that cannot be stored in words - something that lives in your bones, that cannot be codified, but only experience will teach you that, something you know how to do that you cannot fully explain.

   2. Knowing that the same words mean different things in different situations. It’s like a good doctor who does not just read the test results but reads the patient - their anxiety level, their support system, their ability to process bad news, the situation they are in, etc. The treatment plan may be identical, but how it is delivered varies from patient to patient.

   3. The ability to spot the structure underneath the surface details. It is akin to a firefighter who enters a burning building and immediately senses, before they can explain why, that the floor is about to collapse. The reason is that their brain has matched the smoke behaviour, the sound, the temperature against a pattern built from fighting real fires.

3. The interactional expertise that’s not shallow but will grow in value, like

   1. Having the ability to navigate your way through expert communities and send credibility signals. Your capability to operate comfortably inside that world and not just observe it from outside - knowing who to call, what to ask, how to read the room in a room full of specialists, and how to earn enough trust so that the real conversations happen with you rather than amongst them after you leave. You cannot be someone who has used AI to produce a convincing cardiology report and merely use the vocabulary that’s there. You cannot walk into a room full of cardiologists and earn the kind of trust that makes the real conversation happen.

   2. You can be good at talking to people within your own domain. But only a few can genuinely translate - not be a mere coordinator or just relay information, but develop an understanding of what it means in one domain and have the skill to reconstruct its meaning in another domain without losing what is essential. This will become the rarest of capabilities and the most valuable in a complex organisation. It’s not uncommon to see, in organisations today, two people from different domains who are unable to translate their requirements into each other's language and communicate effectively. An example that brings this to life is a hospital's clinical team and technology team working to build a patient monitoring system. Most clinicians know what the system needs to do for patient safety. The engineers know what the system can actually build. The translation failure between these two groups results in systems that are technically functional but clinically useless. This will increasingly become a liability if you are not good at it.

The underlying principle is pretty solid. Real expertise, whether contributory or interactional, requires regular hands-on working knowledge for genuine engagement, and you cannot outsource it to AI platforms or tools.

Therefore, according to a World Economic Forum and Massachusetts Institute of Technology study, by 2035, 39 per cent of key skills currently valued will have changed. By 2075, the dominant professional capabilities may not yet have names. So, be ready to discard, absorb and adopt new capabilities.

So what does all this do to your quest for building expertise?

Here are three questions for you that are worth spending time and thinking about:

• Audit the type of expertise you have been building: In the domain you claim you have competence in, assess if it is contributory or interactional. Analyse whether you can do the work, or engage meaningfully with those who can. Remember, there is no wrong answer. But you need to know what your portfolio mix of expertise looks like today.

• Identify where your interactional expertise has lost its contributory foundation: As you gain experience and grow over time, many of you will find that your contributory expertise decays. Explore specifically, in your core domain, whether there are tasks that AI now handles that you previously performed yourself. If you stopped doing those tasks and AI disappeared tomorrow, could you still do them? If the answer is no, your interactional expertise in that domain is becoming hollow. That is where a lot of work is required.

• Build your orchestration capability deliberately. You need to continuously develop and improve your expertise, at least in your primary domain, to maintain deep and genuine authority to evaluate AI outputs. You also need to embrace interactional expertise to operate across the adjacent domains that your role will require. Therefore, you need to practice the specific skill of directing AI systems using your own critical judgment, which comes with growing contributory expertise. Hence, you must remain open by not accepting the first output you get, not prompting the answer you expect or believe in, but genuinely testing the output against your own independent assessment of what the right answer could look like.

Capability, in any era, accrues value where cognitive effort is sustained and cultivated.

So what does expertise of tomorrow look like? You need the right balance of both contributory and interactional expertise. There is no perfect formula, but it requires constant practice. You must keep doing the hard work in your domain, while building sufficient knowledge across other domains. Also, be ready for them to be tested, refined and trusted when real-world collaboration happens.

Doubt is often read as indecision or a lack of commitment.

This is a skill that is systematically penalised in the corporate world. Not because it is dangerous. But because it is widely considered a weakness.

Over the last 100 years, in the industrial and knowledge era, the professional currency that is respected and rewarded has been conviction. If you are a professional at work at any level, and if you project a certainty-driven attitude and behaviour, you get all the attention and tend to get rewarded more than others. This is not limited to professionals who are just working. If you are a startup founder who expresses an unwavering belief in an idea, you could close a funding round faster. Also, a leader who exudes unshakeable confidence has unquestioned followers. Doubt, when expressed publicly, is often read as indecision, incompetence, or a lack of commitment. So most professionals learn to display certainty - whether or not they actually feel it. Most of them leave their doubt at the door or at home when they come to work.

This trade-off worked pretty well in a stable world. In a rapidly changing one like the one we are currently going through, it is increasingly becoming a liability.

What professionals from the past must leave behind

Most professionals build their identity on two pillars: deep expertise and high conviction. Both have served them well to date. It does not matter if they are an early-career, mid-career, or late-career professional. Expertise gives them speed. Conviction gives them authority. Together, these qualities accelerate careers and help them build teams and organisations.

But as we explored in last week’s issue on deskilling, the very expertise you strive for creates a paradox. As your depth grows, so does your inflexibility. The patterns and schema networks your brain builds to process familiar problems become the very filter through which you screen out unfamiliar ones. You begin to see more of what you already know and less of what is actually new.

Most professionals from the past era were rewarded for never questioning the map - the larger assumptions, frameworks, or models that they were handed over or asked to work with. However, what they must now leave behind is the deeper belief they often hold - that confidence and accuracy are the same thing. The hard truth is that they are not. Ironically, they never were. The gap between projecting certainty- an “I know it all" attitude and being accurate is no longer a harmless career strategy. It is now a professional risk. Let’s explore the reasons further and how to avoid it.

The risk that AI has silently introduced at work.

Before exploring which aspects of these skills you must carry forward, it is worth understanding the new threat that has entered the lives of professionals without their noticing.

Most AI tools are engineered to produce near-perfect and authoritative-sounding outputs. They do not express any kind of uncertainty or doubt. In fact, they sound very authoritative and certain because of the depth of the research and rich analysis that they seem to throw at you. And when you, over time, work repeatedly with an engine that is never seemingly unsure and rich in reasoning, your own internal uncertainty, which is actually your most valuable signal, gets progressively overridden by the tool’s fluency. Your accurate recognition of the limits of what you know quietly begins to migrate to the tool, and you tend to go with confidence that has very little basis in your own reasoning.

Hence, this should prompt you to ask one urgent but vital question: “Is AI silently eroding my ability to know when and where I am wrong?”

Is being wrong or doubtful a loss of your credibility?

Philip Tetlock, a professor at the University of Pennsylvania’s Wharton School, spent twenty years running the most rigorous forecasting research study ever conducted. He studied thousands of predictions and thousands of forecasters. This included people from every domain, from geopolitics to economics. There was one finding, above all others, that proved extremely valuable. The professionals who were most accurate over time were not the most certain. They were the ones most willing to doubt themselves - systematically, deliberately, and in public.

Prof. Philip Tetlock called this cohort of people ‘superforecasters’. He identified one specific habit that made a big difference in all of them. These people updated their probability estimates two to three times as often as average forecasters when new evidence emerged. They actively sought information that contradicted their existing views. And when the evidence demanded a change in their assumptions, they publicly changed their position, explaining what had changed and why. For them, being wrong was not a loss of credibility. It was a demonstration of intellectual rigour.

Prof. Philip Tetlock’s other finding is something you should seriously pause to think about and also reflect on. If you are a professional at work he identified two types of thinkers during his research:

• ‘Foxes’ - who know many small things and update readily

• ‘Hedgehogs’ - who know one big thing and defend it aggressively

Hedgehogs are the professionals the corporate world celebrates. They get speaking invitations on panels or as keynotes, board seats, advisory roles, and invitations to contribute to op-ed columns. They project conviction around what they say and how they say it. In reality, they are, on average, measurably less accurate than foxes. This externally focussed ecosystem creates the so-called experts’ group, who select certainty over restrained calibration. Also, the market for ideas rewards the untested confident storyteller. However, certainty, left unchecked, becomes a systematic liability.

Prof. Tetlock found that smarter people were more capable of generating sophisticated rationalisations for beliefs they already hold. This ‘cage of expertise’ that they have built around themselves is the strongest for those who built it most carefully.

Remember, as a professional, being good at thinking can make you worse at rethinking.

The value of ‘Productive doubt’

Productive doubt is not indecision.

It’s not about being seen as a professional who cannot make up your mind, or as somebody who delays every commitment, or as someone who demands more information before every action — that is not ‘productive doubt.’ That is paralysis. It produces nothing.

‘Productive doubt’ is the deliberate practice of questioning a belief or an output, not doubting yourself. You begin by asking:

• “Is this claim actually true?”

• “What am I assuming?”

• “What would I need to see to update my views on this?”

• “What are the sources of this truth or findings?”

And it is not about saying to yourself, “Am I actually good enough to be here?”

‘Productive doubt’ is about working towards getting closer to what is accurate, not towards anxiety. And critically, it operates in the zone before a commitment, not after you have committed to a solution. It improves the quality of your reasoning at the moment when it still matters.

Today, in a world where AI tools actively suppress this process by delivering confident and rich outputs, it is imperative that you build ‘productive doubt’ through discipline and practice.

Why do some professionals continue to grow while others plateau?

In another very interesting research finding, Adam Grant, the well-known organisational psychologist from Wharton, found that professionals fall into four types of clusters:

• The preacher who defends their beliefs as sacred — any challenge to their position is treated as an attack on their character.

• The prosecutor who attacks opposing views with the goal of winning, not of finding truth.

• The politician who seeks the position that earns the most approval in the room.

• The scientist who holds beliefs as hypotheses, not as identities. They form a view, act on it, observe what happens, and update accordingly.

According to Adam Grant, people with a ‘scientist mindset’ separate two things most professionals normally fuse together:

1. Their confidence to act.

2. Their certainty about being right.

Professionals with a ‘scientist mindset’ have the ability to act decisively on a hypothesis while remaining genuinely open to the possibility that the hypothesis could be wrong. In Adam Grant’s research, the most productive and innovative teams were not led by the most confident leaders, nor by the most humble ones. They were led by professionals who had a strong sense of direction and genuine uncertainty about the method.

How can you improve your ‘productive doubt’ capability at work?

1. Before you commit to a position or a decision, try to think about what would change it: Ask yourself, “What new information or fact would cause me to change my decision or viewpoint?” If you are not able to name one, it is a clear signal that you are not applying enough reasoning, but you are rationalising the decision or position in your own head. This is what Prof. Tetlock calls the difference between a held belief and a defended one.

2. Run a premortem on your strongest beliefs: The premortem practice is widely used in project management and forces you to imagine the project has already failed before you launch and to examine root causes. Do the same for your beliefs or assumptions, as they tend to create bias. Ask yourself:

   1. “What else could be wrong?”

   2. “What am I not seeing?”

   3. “What would a person holding the opposite viewpoint point to as evidence?”

   4. “What is the active proof of this output that I have got from the AI tool?”

3. Test the strongest argument against what you strongly believe: You tend to consume information that confirms what you already believe, like “This is a market that will buy our products.” or “ This pricing will work the best.” Algorithms only make this worse. ‘Productive doubt’ requires a deliberate countermovement. For every position or point of view you hold with high confidence, find and read the strongest argument against it. Try not to get persuaded by it. Use it to test whether your position holds well with the best opposing case. If it does, your confidence is now grounded in something tangible. If it does not, you would have learned something more valuable than being right.

4. Try to delineate your confidence to act from your certainty about being right: Don’t forget, confidence is a communication strategy that you exhibit to others. Certainty is based on your experience, expertise or knowledge. They don’t need to be the same thing, and fusing them is where most professionals, like you, can make your greatest thinking error. It's only fair for you to say “I believe this is the right direction and we are moving on it,” while you may personally hold a view like “I am treating this as a hypothesis and watching for evidence that proves it.” It is not dishonesty. It is a way the best leaders separate conviction from arrogance.

5. Be ready to accept in public, not just privately: This is the hardest step you will find to take and act on. Because you have spent your career in a culture that treats changing your mind or failure as a sign of weakness. Treat changing your position or viewpoint as an act of intellectual integrity, not retreat. The next time you change your position because the evidence demands it, say so explicitly. “I believed X. I now believe Y. Here is what changed my thinking and why.” Over time, this does not weaken your credibility. You can be certain that this will build a reputation for you, which is far more valuable than an image of certainty, as it will greatly improve your accuracy.

Ask yourself, once a day, about the belief or assumption you are most confident or certain about: “What would change my mind about this?” If you cannot answer, that is exactly where the work for ‘productive doubt’ begins.

There has been a lot of discussion and articles about how people like us have to upskill ourselves in the age of AI. However, it may be worthwhile to understand what the impact of people’s skills has been in earlier eras - be it the industrial era or the internet era that has gone by. That will give you a context for the invisible skill decay you or others have gone through, whether you are starting, in the middle, or in the late stage of your career now. This will help you prepare better to handle the scale of change happening around you with AI.

Before we explore how AI will deskill you, let’s spend a minute on the phenomenon of expertise and its inflexibility paradox.

Many of us spent a lot of time and effort building our expertise in our chosen area of profession or function- accounting, sales, marketing, manufacturing, HR, supply chain, IT, etc. But the paradox is that our path to building expertise also simultaneously leads us to a zone of inflexibility. It is important to be cognizant of this problem and work towards not getting there, especially with the rapid proliferation of AI in every job.

Let’s examine why this happens.

According to research, an expert sees too much of what they already know, creating blind spots for themselves. What is funny but stunning is that the same expert sometimes reaches a point where they cannot see what a novice sees more precisely! According to the findings of Erik Dane, a professor at Washington University who led this research, it was found that as expertise grows, the brain becomes dense and forms efficient schema networks for the specific domain. Problems are pattern-matched to existing frameworks. Any problem thrown at this kind of brain system results in a fast, reliable response within the domain. Hence, this creates the following challenges as you develop your expertise:

1. Any novel or new information gets processed through existing, often-used frameworks rather than being processed afresh.

2. An expert tends to zero in on less optimal but familiar solutions over optimal but unfamiliar ones.

3. Their ability to transfer this expertise across other domains begins to degrade.

Therefore, when experts build deep domain mastery and lack deliberate cross-domain exposure, they become professionals who are highly capable within a narrow corridor and get progressively blind to what lies outside their domain.

What’s the performance impact?

If you are such an expert and you are operating in a stable environment, you will have a huge performance advantage. You are quick off the block, can pick up existing patterns in your brain, and solve problems faster than others. However, if you are in an environment of rapid structural change or disruption, which is what the AI transition currently represents, it is a liability. A phenomenon called ‘cognitive stunting’, which was invisible in a stable environment, becomes catastrophic during such a disruptive time.

Beware of ‘cognitive stunting’ and understand how it can affect you.

‘Cognitive stunting ’ is a widely used term in developmental psychology. When applied to organisational work environments, ‘cognitive stunting’ happens when cognitively demanding portions of work are removed from systematic work-related processes or methods, leaving the human with only the routine, repetitive part of that work. Over time, the capability for human beings to perform the demanding work atrophies, not due to laziness but due to lack of use.

The industrial era of the past, which widely adopted Fredrick Taylor’s method of scientific management, systematically deskilled workers’ skills or a craft that they were good at, by decomposing complex, holistic tasks into simple, repetitive blocks of tasks, disconnected from one another. Some of the people then became ‘super specialists’ and many of them became ‘mere operators’. The management layer of the company handled the conceptual and supervisory part of the work. The knowledge era also followed some of these principles, where different teams built different modules of software or hardware. A case in point is the story of secrecy at Apple, which is well known and written about.

In a stable economic environment, all these ‘Isolated ways of working’ worked successfully, while they were actually creating massive ‘cognitive stunting’ of work-related skills and their capabilities in that era. Professionals could still thrive and grow as they were deeply focussed on what they were asked or told to do. They were blissfully unaware of their own ‘Cognitive Stunting.’ This is why you will find people unable to ‘connect the dots’ as they grew up in such an environment, because they looked at a problem or solution only through their lens or scope, yet companies rewarded them handsomely, as that’s how work was organised in the past. So, they had no reason to question or doubt their capabilities or status as experts or operators who could execute well what was assigned to them.

This is really where Erik Dane’s research findings are valuable. ‘Cognitive Stunting’ has created two problems, and this is going to deepen further in the AI-driven world:

1. There will be a group of people who will fail to develop capability due to insufficient challenge. AI will take over many of their cognitive tasks.

2. There will be another group of people who have spent decades over-developing specific work patterns or brain schemas to a point of inflexibility - they are experts or specialists or operators in specific areas. They will now become increasingly irrelevant due to their inflexibility in a new boundary of work if they don’t change.

If machines deskilled people in the past, how will AI deskill you?

In a study released by Microsoft and Carnegie Mellon University in 2025 on the Impact of Generative AI on Critical Thinking, the findings revealed that ‘the more workers trusted AI's capabilities, the less they engaged in critical thinking about its outputs.’ Also, AI users tended to produce a less diverse set of outcomes for the same task than those without AI, leading to suppression of originality and converging towards median-level thinking and solutions. In another study, across mathematical reasoning and reading comprehension tasks, the most disturbing finding was that using AI improved short-term task performance, but when AI was removed, performance dropped significantly, and people gave up sooner too. While AI expedited the answers, it removed the productive struggle that builds both capability and persistence in people.

To summarise, in a company, every workflow where AI handles the hard cognitive step, it will end up actively reducing the capability of the human in that workflow. It will have a direct impact on how you will get deskilled rapidly, in any function or department you are working in.

What are the deskilling signals you need to watch out for?

You will accumulate three kinds of debt:

• Memory Debt: Before the advent of mobile phones, we remembered a large bunch of phone numbers. Can you even remember a bunch of them today? The same will happen with AI. You will forget a lot faster. You will begin to encode problems in a shallow manner. The reason is that your brain now builds fewer and weaker memory structures for the content.

• Reasoning debt: A majority of the reasoning steps will begin to get handled by AI, and you will only handle the prompts and approval. Your critical thinking muscle, the ability to reason through an unfamiliar problem without any scaffolding, atrophies from lack of use.

• Creative debt: Using AI tools does not expand your creative capability, it actually compresses it. However, originality requires the cognitive friction that AI eliminates. You will need to get a deep sense of your work and identify areas where you veer towards central tendency and median outputs.

How can you protect yourself from deskilling?

• Improve your persistence: Don’t settle for the first output you get from AI tools. Develop the willingness to keep trying to search for answers and not accept the ones that come immediately. Ask hard questions. Work through difficulty and look at the problems from different angles to eventually find a breakthrough.

• Learn to encode the friction: Any AI that produces less friction and makes the work easier makes you weaker. The professionals who will win in the next decade are those who will use AI to remove the wrong friction, not all friction. This one singular act helps reduce your ‘cognitive stunting.’

• Reduce your C-R-M Debt: Work continuously towards improving your Creative, Reasoning and Memory Debt. Friction helps improve your creative faculties, persistence multiplies your reasoning capabilities to find the right answers without giving up easily and trying harder, and moving away from habit-led execution to involved cognitive engagement in anything you do improves your memory capabilities.

So what new ways of working can you adopt?

• Attempt any problem unaided first. Then use the tool to check, extend, or improve it. This preserves the productive struggle phase, the phase where your capability gets actually built.

• Before using the tool, articulate your own answer to any problem. Even if it is incomplete, or even if it is wrong. The act of attempting it, wrestling with it, is the cognitive event or effort that builds your capability.

• Try to understand the mechanism and don’t just consume the output. Any tool, when used without understanding its mechanism, creates dependency. Using a tool with an understanding of its mechanism builds your capability.

If you want to protect yourself from deskilling, whether in the current AI era or in the future, refrain from allowing capability to accumulate in any of the tools you use. Remember, capability accumulates where more cognitive effort is spent. Ask yourself one question: “Does this capability still reside in me, or has it migrated from me?”

Why GPA Is Not The Best Indicator of Future Performance!

It’s conventional wisdom that when you look for people to hire, you look for the highest credentials, best grades, most prestigious institution, and the best market compensation to attract them. This is what most companies, startup founders, and HR departments optimise for. Most senior executives and leaders in companies go through this Pavlovian behaviour and are conditioned to it. Hours of effort go into sifting through resumes and potential profiles, shortlisting them, and interviewing a bunch of them by different people within the company before they decide on one. A year or two later, there are these questions and doubts that arise:

• Why did this person decide to quit so early?

• Why is this person not performing as we had expected them to?

• Why is this person not firing as well as we had thought they would?

• Why is this person not fitting into our culture despite all the credentials?

• Why is this person failing when it comes to team dynamics and working?

If you are an individual who goes through these situations, you feel perplexed and confused, as you were always told, “work hard to get into the best colleges or schools, get an awesome GPA, and life is made.” You are also left with these questions: “What broke? Why did this happen? What’s not working? I did all the right things that I was told, but is there any reason why I ended up in this situation?”

Research leads to some surprising but shocking truths.

In an interesting but thought-provoking research study published by Google, where they analysed their internal hiring data, they found that GPA is not correlated with job performance after 2-3 years of work experience. Lazlo Bock, Google’s erstwhile head of people operations, said, “GPAs are worthless as a criteria for hiring, and test scores are worthless - no correlation at all except for brand-new college grads, where there's a slight correlation." If candidates were more than two or three years out of college, Google stopped asking for transcripts and GPA scores. In another landmark 2022 study, researchers found that students were studying primarily to satisfy external demands like parental expectations, fear of failure, and grade competition. These students demonstrated high short-term performance on exams, but their performance deteriorated on complex reasoning tasks where intrinsic engagement was required. The GPA they produced is real. However, the motivation structure that produced it is not predictive of sustained complex performance.

What’s even more fascinating is that another 2024 study, which looked at various research studies over eighty years, further corroborated the above finding. This study, which looked at over 114 research findings across 47000+ data points, confirmed that academic performance predicts early performance, but its predictability weakens rapidly with experience. The study found that the mean correlation between academic performance and job performance was just 4.4%! What is more stunning about the finding is that the insights thrown by the 2024 study are not very different from a 1998 study that was done before this!

This leads to an interesting question: what explains this poor correlation, and what are the best indicators of good performance as people gain experience?

Many studies show that academic performance is merely a good predictor in structured learning environments. However, the real problem is that jobs are not structured learning environments. Another Google study provides some answers to this problem. In a study called Project Oxygen, conducted in 2008, they tried to measure the effectiveness of managers and found that, of the top eight attributes, technical expertise was ranked last. The top attributes that were highlighted in this research were behaviours and interpersonal capabilities, which included:

1. Being a good coach

2. Ability to empower the team

3. Inherent ability to express interest in team members' well-being

4. Having the ability to be productive and result-oriented

5. Communicating and listening well

None of the GPA scores measures these attributes!

Therefore, it’s not about hiring the best people but the right people. For individuals, it is about being aware of whether it is the right job where they can fit.

First, let’s look at the difference between best and right people.

The best people have the right credentials, GPAs, etc. However, what’s the definition of ‘right people’? Jim Collins, author of the best-selling book Good to Great, has an extremely good definition of ‘right people’.

1. Shared core values

2. Are strong on self-management (no tight supervision needed)

3. Have demonstrated exceptional ability

4. Have an ownership mindset

5. And is someone who you would hire again knowing everything you now know about them.

Very often, ‘right people’ is confused with ‘like us’ or ‘like me’ by most people. This is disguised under the name of ‘cultural fit’, which is not right. Most often, this leads to looking for people with a similar social or ethnic background, personal working style, shared leisure interests and hobbies, etc., which loses attention and focus on the top five attributes that Jim Collins mentions as most vital but often get missed.

Another aspect of the right people approach also depends on the kind of company and situation that the role or job requires. Some of the best people can’t work in startups because belief and resilience matter more than a credentialled track record, and there are great startup folks who just can’t perform in a large, structured organisation because they need to constantly influence and align people, need diplomatic skills, and the decision hierarchy frustrates them. Therefore, behavioural and interpersonal capabilities play a more important role than GPAs, as they matter a lot less in these scenarios. Here’s where the person-organisation fit begins to influence a lot more than just grades or scores.

One of the best frameworks to consider while hiring, rather than just looking at credentials or GPAs, is to place the person across a Fit vs Capability matrix.

Apply the football analogy.

A football team selection makes this idea come alive even more.

As a football coach, it is vital to select a striker who can score goals — that is the minimum capability criteria. But if that striker does not follow the tactical discipline or erodes team morale, their individual ability gets neutralised. Many celebrated and successful coaches have done this. Mourinho let Arjen Robben leave Chelsea, Guardiola let Zlatan Ibrahimović leave Barcelona, Ferguson dismantled Paul Scholes’ era — not because these were poor players, but because the fit had broken. But Guardiola’s Barcelona from 2008–2011, arguably one of the greatest club teams in history — had Messi, Xavi, and Iniesta. Not just “right.” Also, genuinely “best.” The winning condition is understanding both are important.

What does all this mean to you?

You may be a high-or low-GPA person, but what determines success in the workplace is how you improve your behavioural and interpersonal capabilities. All these attributes matter even more as you gain experience, and they help improve your performance. The rest can be learnt once you are motivated. Your GPA score is only an indicative factor of your ‘controlled motivation ’-you did it for reward or to avoid punishment. However, your performance at work is an indicative factor of your ‘autonomous motivation ’-doing the work because it aligns with who you are and what you want to do. In this context, a low GPA could be an indicator of high ‘autonomous motivation’ but a poor match to the academic structure that person was subjected to. It does not need any more importance or attention than that. It is also vital to remember that, during your school days, many students who excelled at ‘controlled motivation’ may have resorted to just strategic/surface learning, barring a select few. Most of them would have studied with a focus on the exam, optimised for grade metrics, and performed well in the structure that generated the GPA. This structure disappears in employment, and this gap accentuates with experience. What is needed at work for top performance is a factor beyond the GPA -‘autonomous motivation ’-and asking this one critical question: “Am I doing the work because it aligns with who I am and what I want to do?” Once you answer this question, performance is an outcome with a certain degree of certainty.

Don't Obsess On Technology. Obsess On People.

There isn't a day that passes in companies today when technology isn't discussed in meetings, workshops, boardrooms, management meetings, and off-sites. Every day there is one or the other software tool or technology device that gets peddled as a productivity enhancer, customer experience maximiser, cost optimiser, better decision support system, etc. Of course, more recently, there is the discussion of AI and its impact on business disruption in every meeting. If a company had all the financial resources in its hands and bought all the best-in-class technology that was around, would it have made a difference to its market dominance, competitive advantage, innovation and growth? Let’s look at some examples and reasons, and it’s quite interesting what the answers reveal in reality.

• Toyota had the same machines as Ford. Ford was founded in 1903 and Toyota in 1937. Yet, Toyota became a leader in the land where Ford was founded.

• Nokia had all the technology. Nokia had a 20-year head start over Apple in mobile hardware. Yet, they failed.

• Alibaba had an inferior infrastructure to Amazon for most of its first decade. Amazon was started in 1994 and Alibaba in 1999. However, Amazon had a 5-year head start, yet Alibaba dominated e-commerce in the world's most populous market - China.

• Google published a paper on transformer architecture in 2017. OpenAI built GPT on Google's published architecture. It’s the architecture underlying Claude, Gemini, Llama and virtually every LLM today. Google invented the technology that eventually disrupted Google.

• General Motors produced the first modern mass-produced EV, the EV1, in 1996. But only in 2008, when Tesla came out with the Roadster, did it have a runaway success that led to mass adoption, interest, and global growth of the EV industry.

What do these examples reveal to us?

If a company’s culture rewards self-preservation over reality, then the technology they invented or just deployed will be used to protect careers, not serve customers. Technology is a mere amplifier, and its effect multiplies whenever people are ready. If a company has a bunch of curious people, technology makes them more curious. If they have a bunch of people who are fearful or insecure, technology makes them more fearful and protective, much to the chagrin of a company’s future and its market leadership.

Research studies done over two decades by MIT Sloan Management Review and McKinsey Global Institute show that 70–87% of digital transformation in companies fail to deliver expected business outcomes! In fact, MIT Sloan Management Review puts failure above 87%, and McKinsey puts success below 30%. The data consistently show that the failure rate has not improved over time. During this period, many new technologies have emerged and expanded, and many others have disappeared.

An MIT Sloan Management Review study offers an interesting insight. It finds that cultural resistance is the single most important reason for 90% of transformation failures. It’s not technical debt. It’s not integration complexity. It’s not vendor performance or the availability of the most sophisticated technology tools. All reasons point to the culture. Companies that invest in cultural change alongside technology see 5.3 times higher transformation success rates than those that invest in technology alone. In fact, this is the most under-reported ROI metric in enterprise transformation. Companies rarely budget for this right and often underestimate the pain and cost of this transition.

What really slowed down Google, again, was not a technology failure. In fact, Google has the research, the talent, the compute, and the data advantage. The single biggest factor that slowed them was organisational — the fear of cannibalising search revenue, internal alignment challenges, and the classic innovator's dilemma framework that famous Harvard Professor Clayton Christensen had written about decades before transformers existed.

Another widely known culture failure, rather than a technology failure, was Volkswagen’s attempt to develop one of the most ambitious automotive software transformations in history — building VW.OS, a unified software stack for 10 million vehicles per year. The hard truth was that the technology was credible. But the organisation was not ready. It led to cultural friction between legacy mechanical engineers and newly hired software specialists, which reached a breaking point. In 2024, technical engineers’ turnover hit 14%. The governance processes that worked for physical components, which needed to go through a hierarchy and took more than a week-long approval chain, led to a freeze of software development cycles entirely. It was not a software failure. It was a people-systems failure.

In most technology adoption and transformation programs, it is not rare to see technology budgets increase by 10–20× while the people development budget stays flat. Therefore, the outcome becomes predictable. Companies end up installing a very powerful engine into a vehicle, but the tyres, transmission, and driver remain unchanged. Technology companies don’t tell you this because it dramatically increases the cost of deployment and rollout. They would rather leave it to the company to figure it out, or blame internal company challenges once they get in.

So, what are the learnings, key actions and takeaways?

1. Build absorptive capacity: As an individual, you cannot absorb what you have no foundation for. So, is the case with any organisation too. It is ideal not to wait until the technology reaches your desk to start building adjacent knowledge. It is critical to read laterally across domains that are adjacent to your current work. If you are an individual who has built absorptive capacity, you can be pretty sure that you can adopt ahead of the rest and translate it for the system. Individuals who have not done this cannot, regardless of their openness and higher IQ. Hiring a few specialists and expecting the knowledge to diffuse is bound to fail. In any company, investment in absorptive capacity precedes the technology investment. No technology vendor will tell you that. Fancy words like TCO will be used, but they will never factor these costs in.

2. Link the interaction chain: It is not enough if individuals within a company are curious, open, and willing to adopt technology, the company still will not transform. It’s important to remember that a company is not a sum of individuals. Finally, a company is a system, and right system behaviour emerges out of the interactions between parts—incentive structures, power dynamics, formal processes, informal ways of working, and collective institutional memory—not just from the parts themselves. Hence, these have to be fixed or rejigged as much as the technology that is being adopted or deployed.

3. Define the ownership: Any transformation will succeed when the business unit that bears the cost of the status quo owns the change, not when a central function owns it on their behalf. Business unit ownership of technology initiatives — not technology department ownership — is a necessary structural condition for adoption. Without explicit business unit institutionalisation like new processes, new hiring criteria, new performance metrics, new onboarding practices, changes made by central functions dissolve under pressure. Also, you must treat any resistance or pushback as information. All these signals indicate conditions for adoption have not been met. Either complexity is too high, social proof is insufficient, or risk is unmanaged. Any business leader who treats resistance as an obstacle to be overcome misses the signal.

4. Identify the node multipliers: The individual who adopts and then makes it acceptable and low-risk for colleagues is a critical change agent. Any technology lives or dies with the early majority in a company, not the innovators. Individual change creates the demand for structural change. Structural change creates the conditions for collective change. Collective change then institutionalises the new state. Creating structural nodes between teams that will convert individual change and multiply peer adoption is vital. Remove any structural barriers that act as bottlenecks to collaboration across functions. If you skip any step, the system reverts to the old state. Building a coalition converts individual change into organisational momentum.

5. Show proof, don’t design campaigns: Too often, companies create campaigns or messages that claim an end state even before the technology has been implemented or deployed. There is a lot of focus on creating the message rather than doing the plumbing that is needed for the end-state to happen. Co-opt different teams and individuals and create experiments that show the proof. People need observable proof rather than promises. It is critical to make the results visible, known to others, and not circulate only within the team that is doing the work. Make the proof concrete and highlight the challenges that can impede technology adoption or success. Only visible proof creates organisation-wide momentum.

It’s vital to transform people first, or in parallel, before any company embarks on technology investments, if the company want to realise the full value of its technology deployment. It’s as important for any company to get its people strategy right as it forays into a new technology strategy or deployment. Culture will eat technology for breakfast.

We are all living in interesting times. What we have seen around us over the last three or four years is something many of us may not have seen or experienced in the last few decades. If you were somebody who worked in the last few years of the 20th century, beginning in 1996 and working through the first decade of the 21st century, you would have seen many new industries emerge, existing industries get reshaped, and a whole host of new jobs and professionals emerge. We often forget history and how some of the things we take for granted at work today did not exist a quarter of a century ago.

• The retail industry got reshaped with the advent of the internet. While the internet was a general-purpose technology, the innovations built on top of this stack reshaped how business was done. The evolution of e-commerce, search, social media, ride-hailing, and online travel industries emerged from the internet era.

• Many existing industries like banking, automotive, airlines, travel, retail, healthcare, medical devices, logistics and communications, to name a few, transformed themselves by embedding this technology in their workflows - be it how payments were made, how new automotive technologies were added to vehicles, how travel bookings were done, how medical diagnostics were done and shared, etc.

This led to a whole set of new roles and enhanced professional skills that were not available or there a quarter of a century ago. The professionals who were in the midst of this disruptive era, who either started their careers in the early to late 1980s or in the early 1990s, had to adapt to this new normal. Let’s look at industries and how the composition and mix of professionals in the workforce have changed over the years.

So, what do all these changes mean to you?

Welcome to an era where prediction is cheap

A similar disruption is happening with AI. AI is a general-purpose technology and is starting to affect many industries and is creating the need for new professional skills.

We are now entering an era where prediction is cheap. Cheap prediction does three things in tandem:

1. It commoditises the prediction-heavy roles that currently command premium salaries or take too much time and suck a lot of resources. It’s time-inefficient and resource-inefficient. This problem is bound to break now.

2. It elevates the complementary judgement roles that surround these cheap predictions. An MIS or sales analyst who sends a report to management will be less valuable, but when they share their insights or perspective, they become powerful. A salesperson who is a great operator on the ground but cares a damn about smart, productivity-linked gains from prediction-led sales productivity algorithms will become less valuable. This complementary skill is becoming important.

3. It creates entirely new roles to govern the failure modes introduced by cheap prediction. Imagine you are on a credit risk team at a bank, building models to predict credit scores when income and spending data are available with certainty. If incomes become uncertain or volatile due to more independent freelance roles in society and companies begin changing their hiring policies, you need to learn to work and interpret credit scores in a world of uncertainty where there is a lack of regular income data streams.

Just as the internet era did, cheap, AI-driven prediction is bound to create a whole set of new industries, and existing industries have to transform to adopt it into their ways of working.

Let’s look at some of the implications for you to first understand and appreciate this change, start thinking and know what it takes to prepare for it:

Implication #1:

Companies will transform to have smaller headcounts but not reduced capabilities. It is bound to have fewer people doing more consequential or impactful work. The ratio of seniors to juniors is expected to shift dramatically - some of the junior or administrative or operational roles that were valuable for their volume of routine tasks (an MIS analyst, supervisors, programmers, maintenance engineers, project coordinators, etc. ) will collapse. If you are playing a mid- or senior-level role, your expertise and judgment in rare and complex situations become more valuable and will become more fully utilised. However, if you are merely running operations or coordinating resources and projects, whatever your designation or role may be, your role is bound to get disrupted if you don’t upskill yourself to move up the critical thinking value chain.

Implication #2

When you look at history, new roles tend to aggregate into new institutions or industries. The printing press, a general-purpose technology, created publishing houses, libraries, and eventually mass media organisations - companies or institutions that played a role in how they channelled the curation of abundant text and media produced by the printing press. The calculator and computer created investment banks, hedge funds, and actuarial firms, institutions designed to improve quantitative decisions. Look for new industries that could be created by this change. In the past, during the internet era, those who sensed and prepared and got on the bandwagon ahead of others reaped the benefits. It’s imperative you do it now. You need to greatly improve your ‘sense and respond’ capabilities to identify such hidden or emerging opportunities.

Implication # 3

It is important not to get swayed by sensational conversations and hype. Most of the conversation and chatter in the media, and in most articles, seem to ask one question: “What will AI replace?” This is the wrong question to ask and a wrong way to search for answers. Instead, ask yourself this question: “What does this make scarce?” Take, for example, the internet. The internet made information distribution cheap. Before the internet, reaching an audience needed a printing press, a broadcast licence, or a distribution network. These were expensive bottlenecks. The internet shrunk the cost of distribution to near zero. Anyone could publish, reach, or connect with anyone. Therefore, what became scarce with the rapid proliferation of the internet was attention, trust, presence and depth. The companies and professionals who took advantage of these scarcities became valuable. The industries that emerged from this scarcity were programmatic ad tech and marketing technology ($500B+), influencer marketing ($21B), streaming ($80B), Cybersecurity ($500B), Online education ($ 200B), Professional Coaching ($20B), Mindfulness and Meditation ($4B), etc. This led to new roles such as SEO Specialist, Growth Hacker, Social Media Manager, CISO (Chief Information Security Officer), Data Protection Officer, etc. So, start asking this question: “What will AI make scarce?”

Implication #4

What you knew over the years will become less relevant, what you learn that you don’t know will become very relevant. It will become increasingly vital for you to understand how your existing role will evolve and the whole set of new skills you need to equip yourself with. These are subjects or concepts that may not have been taught to you at school or in your past work experience. Because these did not exist when you graduated or completed school or when you were working. Take, for example, a production engineer on the shop floor. They spend almost 60% of their time on tasks that are, at root, prediction problems:

• Did this batch meet specification?

• Will this machine need maintenance?

• What is causing this defect pattern?

• What is the optimal production sequence for this week's orders?

Cheap prediction will soon eliminate all of this work done by humans. They need to enhance their skills in redesigning the process layout, introducing new manufacturing technologies, and resolving recurring exceptions that keep reappearing because the prediction system cannot yet handle them.

It is vital for you to start asking questions specific to your role that current prediction systems don’t have the requisite data to predict and how your tacit knowledge will add value to the predictions that these prediction systems will throw up.

Implication #5

It will be important for you to understand which skills will become increasingly premium as prediction becomes cheaper in the next few years. Here are a few that are likely to become important:

• Not just data literacy is enough anymore, but data philosophy will become valuable. Data philosophy is the art of understanding the data from different angles and contexts, not just numerical literacy - the underlying meaning behind the data, questioning some of the metrics as to whether they are needed at all as AI generates most of them at quick speed, spotting a data anomaly, etc.

• Thinking quality will become premium. If AI can do most routine tasks, compose written documents, write code with prompting, and predict at scale, the real bottleneck shifts from writing or coding to the thinking that precedes them. Your ability to articulate your thinking will become valuable, as AI agents will do the tasks that you define and put down clearly.

• Being an orchestrator will be a valuable skill in any role or function in any company. You must develop the ability to direct multiple AI systems effectively toward a complex goal and have a deep understanding of which AI capability to apply to which sub-problem. Developing contextual sensitivity - reading the specific dimensions of a situation will be vital as AI models are trained on historical data. AI agents will be blind to the current situational context. You must envision yourself like how a conductor is to an orchestra - not playing any instrument, but directing the integrated system toward a unified outcome.

Irrespective of any role you are in today, cheap prediction will collapse three things: routine data gathering, routine pattern recognition, and routine decision execution. One pattern is consistent across five centuries of cognitive technology. Each wave has not reduced professional value. It has only moved professional value upstream, to a higher level or function that the technology could not reach, as yet. Cheap prediction will force you to move your value of work upstream to a higher level. The ones who do will grow, and those who resist will become obsolete.

Idea Starters For The Week

Rajendran Dandpani on the importance of practical, hands-on learning over traditional academic metrics.

• Encourage students to pursue their interests actively rather than passively receiving information.

• Why cultivating real-world skills is vital.

• Find one’s true calling, rather than merely chasing jobs.

»YouTube | Apple Podcasts | Spotify | Amazon Music

Revisiting first principles from past editions

Think Of Work Like A Sport

When we look at sports, there are some great lessons we can take to our business, profession, and workplace.

• We should learn to compete with ourselves.

• Build better mental skills to handle risks and setbacks.

• Become the “CEO of Your Own Performance.

Explore more here.

The ContraMind Code Thought For The Week

Patience Is Not Absence Of Ambition

Patience is the most misunderstood trait or skill.

In the workplace, patience is sometimes seen as a sign of weakness, as it makes a person seem outwardly non-aggressive and slow to make decisions. Often, such people are seen as soft, tentative, or lacking the drive or energy to move fast and decisively.

What is patience? Is it a trait or a skill?

Patience is the capability of sustaining purposeful action toward a long-horizon outcome while remaining fully in control and also aware of the short-horizon environment around that is not yet rewarding the direction one seeks.

Patience is both a personality trait and a learnt skill. Patience is partly a temperament, a raw, biologically based, individual-specific emotional baseline on how they react or respond to anything, and partly a practised skill, built through deliberate effort and training.

The Emotion of Impatience

According to Kate Sweeny, Professor of Psychology at the University of California, Riverside, in one of the most recent research findings on patience, concluded that “Impatience is the emotion — triggered when we face unwanted delays. Patience is not a trait that some people have, and others lack. It is the name given to the self-regulation tools applied to cope with the emotion of impatience.” What does this mean in the real world? This means that emotion regulation is a skill, and you can train yourself to become more patient.

What does impatience do?

Impatience destroys your professional life. It forces you to jump to wrong assumptions. It damages the quality of your subsequent decisions. Therefore, you increasingly react to surface signals rather than to real information.

The well-known Stoic philosopher Seneca offers a brilliant analogy to bring this behaviour to life. His analogy of a ‘court of law’ and a ‘mob’ illustrates the true value of reason when one practices patience. Seneca writes, “Reason behaves like a judge. It grants a hearing to both sides, it seeks to postpone its own decision until it has adequate evidence, it aims for a conclusion that is actually just.” However, a mob “refuses to hear evidence, it loves its error and clings to it, it will condemn the accused because it dislikes the advocate.”

So, impatient professionals get sucked in, driven by whatever the environment sends as signals, and become reactive rather than responsive. On the other hand, patient professionals don’t get pulled in but can stay outside the problem or decision they want to make. It’s not because they want to be indecisive, but because they understand the need to control the timing or judgment in any given situation before making decisions.

Therefore, every significant decision you make—whether it's a talent call, a product decision, a strategic pivot for your business, your response to a market signal, or even a mundane incident like a challenge from a peer—is either a court proceeding or a mob verdict. If you are a professional who has built patience as a practice, you will default to how a court behaves. However, if you are a professional who responds urgently to everything that comes to your table, you will default to mob behaviour.

But does patience produce success in real life?

Here are professionals who have demonstrated the value of patience in both global and APAC contexts.

The story of Dr Frances Oldham Kelsey, a leading pharmacologist and physician who had a 45-year career with the U.S FDA, is simply inspiring. It is a masterclass in practising patience in the face of extreme professional pressure and the potential damage to her professional reputation. It’s the case of thalidomide, a drug application that reached her desk. Under U.S. law, the FDA had just 60 days to reject a drug application. Any failure by the FDA to act meant automatic approval. The interesting point that Kelsey found in the application was not evidence that the drug was dangerous. It was the absence of evidence that it was safe. After further analysis and review, she rejected the application and requested stronger evidence. The company Richardson-Merrell resubmitted the case again. She rejected it again.

Over nineteen months, the company made approximately fifty visits to the FDA office, escalating pressure through corporate representatives, then legal channels, then political contacts. She did not relent. In her own words in an interview several years later, she said: "Here was a drug that looked like it should be no problem, but at the same time there was just a feeling that there was something in the data — or the absence of data — that was a cause of concern." The key point here is she was not waiting for the data. She was refusing to act until it arrived. The urgency to act did not ruffle her. The answer to her doubts happened when disaster arrived in November 1961. It emerged from medical reports in Germany and Britain of an epidemic of severe limb deformities in newborns. The cause was thalidomide, taken during early pregnancy! Kelsey did not retire after the thalidomide case. She remained at the FDA for forty-five years — until 2005, till the age of 90!

The now well-known ‘Metro Man’ from India, E. Sreedharan, began his career in the Indian Railway Service of Engineers (IRS) in 1953 as a Probationary Assistant Engineer. Over the next thirty-seven years, he was transferred at least twenty-five times! It is phenomenal that neither the work principles he stood by nor the tribulations he endured received external recognition during those years.

When Sreedharan was given India’s first metro project, he drew his operating philosophy from the Bhagavad Gita's Karma Yoga framework - ' Do the work, do not seek the outcome.’ This is by no means passivity at work. It is the precise cognitive framework Seneca's De Ira recommended — decoupling action from urgency, decoupling effort from the demand for immediate validation. It’s a widely reported story about how Sreedharan had a reverse clock installed in every Delhi Metro project office, counting down the days to the deadline. The countdown was visible. The patience he practised was structural. Time magazine listed him among Asia's Heroes. The Government of India awarded him with the second-highest civilian award, the Padma Vibhushan. All these recognitions he got did not come during the thirty-seven years of his invisible professional career. It happened after his retirement!

Another great example, closer to home in this part of the world, is the patience practised by the well-known scientist Dr Raghunath Mashelkar. He joined the National Chemical Laboratory (NCL) in Pune as a scientist after completing his chemical engineering degree. He went on to build what would become India's first internationally recognised polymer science research programme. It was 16 arduous years of diligent, systematic work that led him to become the director of NCL. He was only the third Indian engineer in the twentieth century to be elected a Fellow of the Royal Society. He holds 53 honorary doctorates — more than any Indian engineer before him!

Here is the most incredible moment of his career that is less cited or even discussed anywhere. In 1994, a U.S. patent was granted on the wound-healing properties of turmeric — a remedy documented in Sanskrit medical texts for centuries. Mashelkar led a 14-month legal battle before the U.S. Patent and Trademark Office, presenting ancient Sanskrit texts as prior art, and secured revocation of the patent. He then did the same for Basmati rice, challenging a Texas company's patent before the U.S. Patent Office. The combined outcome of these two wins forced the World Intellectual Property Organisation (WIPO) to revise its International Patent Classification System for the first time in decades, creating new subgroups specifically for traditional knowledge.

So, how do you practice patience and get better at it?

• Learn to differentiate between ‘True urgency’ and ‘False urgency’: Well-known Harvard Professor John Kotter’s research offers valuable insights into how to understand and work on them. According to Prof. John Kotter, “ True urgency is purposeful, evidence-driven, and directional. However, False urgency is rooted in anxiety and anger — it produces frenetic activity without productive results. It looks busy, sounds urgent, and generates enormous energy. But it is not aimed at the critical objectives.”

• Know how to absorb the delay: Delays are inevitable. Not everything will happen when you want it or the way you want it to happen. Don’t forget Seneca's single most repeated and most urgently recommended technique - deliberately insert time between provocation and response.

• Understand that not all patience is equal: Prof. Sarah A. Schnitker's brilliant research provides deeper insights into the different types of patience, which will enable you to handle each of them better :

  • Interpersonal Patience: Develop the capacity to remain emotionally controlled and patient when dealing with difficult people. It is active emotional regulation in response to social provocation. Don’t worry if others misunderstand this as you being passive.

  • Daily hassles patience: This is your capacity to remain calm amid the accumulation of minor frustrations, like a meeting, an email, a failed process, or a colleague who underdelivers.

  • Life hardship patience: This is your capacity to maintain mental poise and exhibit strong character in the face of chronic adversity, such as career stagnation, organisational dysfunction, and systemic obstacles.

• Refuse to leave the system when it is unrewarding: The easiest act you can display is to quit when things are not going your way, or you are not adequately rewarded for your contribution. You must develop the ability to hold your conviction without urgency — not merely passively, but without demanding immediate validation. Develop the muscle to handle the extreme contradiction you will experience in your mind, and be ready to push it to the edge as much as possible.

• Don’t act with urgency when you don’t have strategic clarity: It's common to see urgency in many projects or initiatives in companies without conviction. This is a precursor to structurally breaking your patience. You will need to develop the capacity to hold a strategic conviction for extended periods when people do not believe your viewpoints and the environment has not yet validated them.

When should you act with impatience?

Not all patience is good, too. There are situations when you need to act with impatience. This could include situations:

• Where ethics, safety, or integrity is in question

• When complacency is rampant in the system

• When the window of opportunity in the market is genuinely closing

• When purpose-led patience is leading you nowhere

• When passive behaviour is hidden under the label of patience

• When relationships require honest confrontation

• When your growth requires discomfort which you have been avoiding

It is important to remember that patience is the meta-skill that sits underneath resilience, self-awareness, deeply held values and beliefs, controlled timing, delayed reasoning, evidence-led and the ability to hold your strategic conviction in the most testing times. Don’t confuse patience with passive behaviour, and ambition with urgency. The patience architecture has high ambition and low urgency at its core.

Some of the lessons we learnt from this week’s mission:

1. In the real world, skills triumph over scores.

2. Learn to compete with yourself.

3. Don’t confuse ambition with urgency.

Starting this week, there is a change to the newsletter format in response to the feedback I have been receiving. I am discontinuing the structure of sharing articles, podcasts, and videos that I have either read, listened to, or watched during the week.

One overarching piece of feedback was a request to share my personal perspectives, idea starters, experiences, and learnings on topics I have found valuable for personal or professional growth. Especially the ones that have forced me to reflect and think hard on them. Accordingly, the newsletter's structure has been changed.

I look forward to your feedback and continued inputs to make your reading experience more engaging and better.

Thanks for reading,

Swami

Idea Starters For The Week

Logan Yonavjak on why great strategies fail without ready leaders:

“Strategy succeeds only when leaders have the capacity to execute it. Leadership readiness matters as much as strategic thinking.”

“Self-awareness is the hardest leadership skill.”

» YouTube | Website | Apple Podcasts | Spotify | Amazon Music

Revisiting first principles from past editions

How to avoid a midlife crisis.

View your career through a 30-year working lens, not through the narrow focus of your current job, and prepare yourself accordingly:

• As you age and gain experience, ‘synergy takes precedence over energy’.

• Find alignment between what you want to do and what you do.

• Don’t confuse annual recognition with improved job capability.

• “When your skills become uncompetitive with your compensation and capabilities, companies and the market will be the first ones to dump you!”

Explore more here.

The ContraMind Code Thought For The Week.

Stop Building Consensus. Start Building Productive Opposition.

The most dangerous professional habit that you will see around in company meetings, discussions, and in boardrooms is the habit of consensus addiction.

It’s not rare to see a slow shift in such situations where everyone agrees, debates are polished or diplomatic, and friction gets managed rather than encouraged. The most commonly observed failure in companies is the absence of constructive conflict - it is relatively minuscule and of the right kind. In fact, a quiet but uneasy atmosphere of harmony pervades the workplace, and it remains an unaddressed problem.

One of the many reasons for this kind of situation is the inability of the people in these companies to differentiate between ‘task conflict’ and ‘relationship conflict’. Most of the time, a majority of the people confuse one for the other or are unable to differentiate between the two. They treat both of these conflicts in the same vein without any difference.

• A ‘task conflict’ is one where two people have a disagreement about ‘how to do’ something. It could also fall under the ‘what to do’ and ‘why to or why not do it.’ Here, the primary conflict, while broad ‘need-to-do-it’, is aligned, but the timing, approach, effort and intensity, resources - both financial and personnel to be used, and the way of doing it will be in conflict.

• A ‘relationship conflict’ is one in which there is a ‘disagreement among the people involved.’ Here, there is a breakdown in the relationship, with trust and confidence in question, and the task at hand is not the core problem. The people involved don’t see eye to eye. The underlying mutual professional respect, which is vital, is not there between the individuals.

If you have a ‘task conflict’, it can improve decision-making. However, if you have ‘relationship conflict’, it destroys collaboration and teamwork. In the case of a ‘task conflict’, the outcome for the people involved is to reach an accurate solution through debate and argument. Here, the solution to the problem is not achieved by one person dominating the other, but through convergence via productive counter-viewpoints.

Most people and organisations often see disagreements as friction. They find it hard to see it as a way to discover alternative viewpoints, or as a method to agree on the final result they want, and to discuss different approaches among the many possibilities to reach that end result.

Therefore, how can you begin to practice this at work? Is there a way to start thinking about this problem differently and put it to work?

Understand the power of ‘adversarial collaboration.’

Well-known psychologist and a Nobel Prize winner in Economic Sciences, Daniel Kahneman coined the term ‘adversarial collaboration’. He used this term more in the context of research - to improve research, from problem definition and research design through to outcomes. It is a technique to remove bias at the point of designing the experiment, which is where, in his view, motivated reasoning most heavily distorts science (and organisational decisions).

One of the most profound insights Kahneman found from his own collaborations was that bias starts right at the problem design or definition stage. He mentions that researchers unconsciously select experiments likely to confirm their existing view. ‘Adversarial Collaboration’ forces the adversary, in this case another person who is a collaborator in the research, to propose experiments that challenge them, which exposes the weakest parts of their own theory. In a 2025 study, projects with adversarial collaborators led to a deeper understanding of the problem.

Another interesting but often-quoted example is Dr Alice Stewart, a well-known British Physician and epidemiologist, and her statistician colleague George Kneale. They formed one of the most well-known but highly effective scientific partnerships of the 20th century. The duo is celebrated for using "constructive conflict" to reveal severe dangers of low-level radiation. George Kneale once said, “My job is to prove Dr. Stewart wrong.” They collaborated for decades. But opposition was the method of their working!

When you apply the concept of ‘adversarial collaboration’ in the context of organisation or company and people dynamics, you have to come to terms with the fact that you cannot change the minds or belief systems of people by winning arguments. Most often, in companies, you will see people are hired for agreement:

• “Can the person fit our culture?”

• “Can this person take the team along?”

• “Does this person demonstrate capabilities of being a people’s leader?”

While this is the final outcome you want - somebody who largely fits the culture, can work with people well, etc.- the ‘how’ behind these questions is not always thought through deeply enough. Therefore, senior leaders or managers tend to surround themselves with, or hire, people who conform to their existing views.

If you were to contextualise this using Kahneman's analogy of the research team of adversarial collaborators, you would end up hiring for the strategy or results you already believe in.

Therefore, if you are a founder, a CXO, leading a function or department, or even heading a small team, the adversarial collaborator on your team could end up being the most valuable person in the room. The person may not be the most agreeable for many people in the room. They may or may not even be the most credentialed in terms of designations or hierarchy. Building such an organisational structure within your teams and providing space for such an empowered relationship is a strategic act, not a cultural nice-to-have.

So, how can you build a culture of ‘adversarial collaboration’?

• Create shadow board members or an invisible hierarchy: If you are a senior leader or CXO, identify three to five people who will consistently disagree with you, also they must have no stake in your success, and possess the expertise to be credibly wrong. However, if you are a middle or entry-level executive, create this invisible hierarchy of advisors or your personal advisory board who will challenge your work. You can meet them quarterly. You can brief on strategic decisions before they are made, or on the work you are currently thinking about or doing. You can act on objections with the strongest evidence base these people throw at you.

• Embrace challenger briefs: Before committing to any strategy or project, work with your team to create a two-page challenger brief that argues the strongest possible case against the decision or approach — using the same data or information you used to make it. Create a safe space for people to question or debate the decision or approach through conversations, meetings or discussions. You can then score it. If there is a weak opposition, you can proceed with your decision or approach with confidence. If there is a strong opposition, you can revisit your assumptions.

• Evolve solutions to problems through competing teams: It’s normal for people within teams to draw their own boundaries of what to work on or not. This also develops an entitlement mindset among team members. Also, other team members or groups are not allowed to take a shot at a problem they feel they are better at or for which they may have a different approach. You can break this method of working by creating competing teams working on a problem. One team’s job is to defeat the other team's strategy — not refine it, not improve it, but break it. This forces a fair test of the ‘adversarial collaboration’ design framework in an operational form on the ground.

By implementing some of these ideas, you have a chance to become the professional who actively engages credible opponents and avoids seeking consensus. It can also narrow the gap between your belief and reality — while everybody else waits for the market to show it for them.

A consensus-driven approach creates a bunch of look-alike thinkers and doers. Therefore, the question to ask yourself is “Do I have the nerve and inner strength to handle disagreements that question my view or hypothesis?” The ability to handle the answers and leverage such intellectual conflict is vital.

Some of the lessons we learnt from this week’s mission:

1. Self-awareness is the hardest leadership skill.

2. As you age, it may be vital to remember that synergy takes precedence over energy.

3. You understand problems more deeply when you can handle constructive conflict.

The Old World Is Dying.

This is an article by Jasmine Sun, a Stanford University graduate( graduated a few years back) and an independent writer who writes about AI and Silicon Valley.

In her recent Substack article, she writes that it’s not as scary for the twenty-two-year-olds as it is made out to be! She shares her own experience and perspectives. Some of the thoughts that make this article an interesting read are the ones that can make you think and question commonly held beliefs and views.

Here are some of the questions that she poses, which are quite provocative, and they appeal equally to anybody who is working today:

• “Freelancing is as secure as employment”

• “Founding a startup is as secure as a big tech job”

• “Young people know as much as seasoned professionals”

• “Physical labor is valuable; cognitive work is cheap”

• “Nothing is sacred; everything is fallible”

She asks three fundamental questions to prepare and succeed in this ever-changing and disruptive world out there:

• ‘What is your comparative advantage as a human?’

• ‘What traits will be scarce?’

• ‘How can you stay a complement to machine intelligence?’

Read the entire article here.

How Boards Have The Best Conversations To Make The Best Decisions.

On the Enter the Boardroom Podcast, Oliver Cummings is in conversation with Prof. Emy C. Edmondson of Harvard Business School about applying the concept of psychological safety to boardrooms.

What was especially valuable in this conversation was the definition of psychological safety, which is often misused in corporate environments. Prof. Emy unpacks the definition very well. And her three questions to assess the quality and depth of boardroom conversations are very intuitive and insightful.

Here is a snapshot of some of the ideas that are discussed in this conversation. Take time to deep dive and listen to this conversation. There are many takeaways to help you apply the concepts as a leader or manager.

• Psychological safety is an environment where people believe it’s safe to speak up honestly with candour. According to Prof. Emy, “It’s a sense of permission for candour.”

• She goes on to explain this a lot better and more powerfully: “It is an environment where people are more concerned about the quality of the conversation than about how they look.”

• Psychological safety does not mean you are sacrificing “the goal of performance” over “the goal of inclusiveness.”

• To effectively measure the scale of where you stand as an organisation, she suggests three questions to score against:

  • “What’s the quality of our conversations?”

  • “Are they fully engaged?”

  • “Do you have a subjective sense that you are getting somewhere? Progressing forward?”

How To Break Bad Management Habits Before They Reach The Next Generation Of Leaders | Elizabeth Lyle.

Elizabeth Lyle is a Managing Director and Partner at BCG, an HBS MBA, and leads BCG's Leadership & Talent Enablement Centre.

In this TED Talk, she talks about how middle managers, or people who are going to be next-generation leaders, need to avoid getting stuck in the old-fashioned or outdated habits of their senior leaders. She reiterates that they cannot wait for things to change till they take the mantle of leadership, as by then some of these bad habits get deeply ingrained in them. And often they settle into ways of working so that they don’t rock the boat, and that leaves them with serious implications in the future.

She proposes a way for middle managers and senior leaders to work together. She makes a very important point that a conscious effort to develop their leadership style or method, which is in tune with the current realities of running an organisation, has to be made in their formative years. The groundwork of how they want to lead needs to be put in place early, and that’s the real challenge for them. It’s imperative that the next generation do this because it may be too late by the time they learn to lead the organisation of tomorrow, and they then could be staring at a situation where they may be without a job.

This can happen only when senior and next-generation leaders step out of their comfort zones and learn to navigate the contradictions in their interactions and approaches to leadership and decision-making. It has to happen in-flight, as they work every day, not in training rooms.

Are You Suffering From Seniority Paradox? Take The Smell Test.

As you become successful and move up the hierarchy, you will begin to realise that fewer and fewer people are willing to tell you uncomfortable truths. It’s akin to you sitting, as you grow in your career, in a soundproof room. What’s more, whatever information trickles to you would be heavily curated by others or people reporting to you to fit into what you may want to hear or believe.

One of the reasons people don’t tell you uncomfortable truths, and why you may sense an eerie silence in your hierarchy chamber, is that it starts with how you behave, process information and operate as a leader. Your past success and the methods you adopted to get there act as major inhibitors. Your behaviour, then, is driven primarily by a need to defend your position because you have already invested too much in proving what you know and protecting your expertise. You want to avoid being seen as someone who doesn't know.

What explains the reasons for such a behaviour pattern of senior leaders?

The Seniority Paradox - The problem of skilled incompetence.

This phenomenon, developed through his famous research by Prof. Chris Argyris, is called ‘skilled incompetence ’- where the more competent a professional becomes at performing within a system, the more competent they simultaneously become at protecting that system—and themselves—from the information that would require it to change. Ironically, the same intellectual capacity that makes a senior leader excellent at executing a known strategy makes them excellent at avoiding the feedback that would tell them the strategy is wrong!

This leads to a predictable behaviour pattern in senior leaders:

• They start practising unilateral control. The leader first decides and then seeks inputs to validate, not challenge, what they are doing or planning to do.

• Any challenge to the leader’s view is seen as a competitive threat rather than information.

• Any bad news is appropriately curated by others before delivery. Very often, views expressed with a certain amount of honesty get labelled as "unhelpful."

• Any form of emotion or dissent is treated as irrational.

This behavioural pattern creates a default operating system in which every senior professional or leader prioritises control and winning over the truth. This creates an environment, or an ecosystem, in an organisation for filtering information or messages at the top. What also begins to happen is that such senior leaders fall into what’s a ‘single loop trap’, where they begin to fix problems without questioning the assumption that produced them. They then fall into a ‘doom loop’ that leaves them feeling embarrassed and threatened by the prospect of critically examining their own role.

What does this do to young, emerging leaders?

1. Subordinates learn to pre-filter what they say before they say it. They learn from multiple experiences as to what happens to them when they don’t do it. They learn to edit the information or messages themselves.

2. They begin to show two modes of behaviour towards their interaction with senior leaders. ‘Defensive Silence’ - They withhold information entirely, and ‘Defensive Voice’ - They say something, but something that’s safe, sanitised, and already known to be acceptable.

3. They resist taking any intellectual risks. This leads to low engagement, risk aversion, low accountability, low interest in innovation, and self-censoring of new ideas or thoughts.

How do such senior leaders impact the careers of young, emerging leaders?

• Career Plateau: When young but highly potential and ambitious leaders work under such senior leaders and hierarchy, they go through years of suppression of their own agency and thoughts on how they would have got things done. This can happen to them across organisations as they move from one to another over their career. Therefore, their plateau isn't caused by their performance but by the absence of opportunities and visibility that a different leader would have created. Such leaders inhibit the display and deployment of their potential in response to problems or opportunities, leading to frustration, burnout, loss of interest, and alienation, resulting in detached interest in work. Their careers plateau far earlier than they should.

• Lack of visibility to management: The senior leader, since they exhibit unilateral control behaviour, does not amplify their subordinates’ contribution upward. The subordinate's best work either becomes the leader's demonstration of their own judgment or is suppressed because it would surface a problem the leader doesn't want to surface.

• Knowledge hoarding and collaboration breakdown: Young leaders’ behaviour of ‘defensive voice’ and ‘defensive silence’ during their interactions makes them protect the knowledge or information they possess rather than share it. They become fearful that sharing creates exposure and transparency creates a feeling of vulnerability in them. Long after the leader is gone, this becomes career-limiting for young leaders as they unknowingly settle into this de facto behaviour.

• Familiar Pattern transfers and compounds: These leaders create a huge downstream impact on such young leaders. Shaped by years of working under senior leaders with the ‘skilled incompetence’ phenomenon, these young leaders get promoted and begin to reproduce the same pattern one level down — often without any conscious intent to do so. They become the next ‘skilled incompetent’ leader, passing the damage to their own team.

If you are a senior leader, what steps do you need to take to break the seniority paradox?

To genuinely change, the important prerequisite is that, as a senior leader, you must develop the ability to hear what you may not want to know. Here are some steps that you can take to work on this problem:

1. Name your defensive behaviour, in public, to your team.

2. Audit your reaction pattern — not your intention.

3. Write down your governing beliefs and test whether you stuck to them when you were under pressure.

4. Periodically invest in your own 360-degree review( not done by the company you work for), and do it with people you have worked with or reported to across your current and past organisations.

5. Ask yourself one question in an organisational context: “What are the topics in this organisation that everyone knows about and no one discusses?” and find an appropriate reward system for those who bring such topics consistently.

To break your seniority paradox, it starts with one personal but critical question: “Are you structurally ready for demolition and rebuilding?”

Trapped in Your Business

Andrea Chiarelli, Software Engineer, Technical Author, Identity Practitioner, writes his review of the book The Trapped Operator by Peter S. Bergeron and his takeaways on why small-business founders burn out while running their companies are brilliant. There are some very powerful insights and observations that he makes from the book:

Here are some takeaways that will make you think and reflect on the points he makes:

• “If we treat each problem as a discrete event, we end up addressing the same problem repeatedly, in slightly different disguises. A cash crunch is solved with a new line of credit. Several months later, the same crunch returns, this time magnified by the debt accumulated from the last one.”

• “Stop asking “What caused this?” and start asking “Which underlying system is failing to contain the pressure that is producing this?” The two questions sound similar, but they produce entirely different conversations.”

• Business does not go through the traditional lifecycle of growth, maturity, and decline, but it goes through a lifecycle of survival, scale, and stability.

You can read his article here.

Why High Performers Burnout, And How To Stop It.

In this conversation on the ContraMinds Labs Podcast with Nick Petrie, a renowned researcher and speaker on leadership, resilience and burnout prevention, he brings out salient points about burnout based on his 3-year longitudinal research study and his recently published book ‘Burn Right’.

Here are some key takeaways from this chat:

• Most people don’t suddenly burn out. They move through different degrees of burn—from temporary stress to chronic overload to complete exhaustion.

• The best performers don’t work at maximum intensity all the time. They “pulse” between focus and recovery, effort and renewal, just as nature operates in rhythms.

• The experiences that develop leaders the most are often the uncomfortable ones. Stretch assignments, uncertainty, and first-time challenges build capability and resilience.

• Pressure is inevitable. Stress often occurs when we repeatedly replay negative stories in our minds. Reflection helps us learn; rumination keeps us trapped.

• People don’t burn out only because they work too much. Many burn out because they spend years doing work that conflicts with what they truly value.

You can click on the above link and listen to the entire episode.

"A.I. and Our Economic Future," Professor Chad Jones

Professor Charles "Chad" Jones explores the transformative impact of artificial intelligence on our economic future. He makes some very compelling arguments by studying the impact of transformative technologies over the last 100- 150 years. He puts forth the idea of ‘weak links’ that will act as a bottleneck for high growth and widespread job losses or disruption in the next five years. Prof. Charles Jones reinforces his argument with a simulation model that accounts for GDP growth. Labour’s contribution to GDP growth and Return on Capital contribution to GDP growth over the next 75 years. He builds his hypothesis around two key statements that he applies to the simulated model:

1. Scenario 1: AI dramatically accelerates economic growth.

2. Scenario 2: AI is just a normal technology. AI is business as usual.

He tries to answer the question: “Does growth and change happen in three years or five years, or does it happen in 25 years?”

• Prof. Charles Jones makes an emphatic point that even though the steam engine, electricity, and the internet were wildly transformative, growth rates were still 2%.

• It’s complementary innovations on top of these disruptive technologies that drove this growth; else growth would have slowed - semiconductors, electric motors, internal combustion engines, spreadsheets, computers, etc. These complementary innovations need to be integrated and reorganised into the production process.

• He then brings forth the concept of ‘weak links’ - Business success requires completing many, many tasks successfully, and one weak link can stall the success. “Chain is only as strong as its weakest links.”

• He also makes a point - “Computers are getting less of GDP, not more of GDP, even though there are 100 million times more transistors in your pocket.” “Software is about 2% of GDP.

• “We have more radiologists today than we did in 2016, and they’re paid more than they were in 2016.” This is because of the weak links.

• The weak-link view suggests that things take much longer than you think.

You can click on the above link and watch this video.

A Named Problem Is Not The Problem. It’s Merely A Signal.

One sentence from Andrea Chiarelli's article that sounded very powerful but true was the one about “A named problem is not the problem. It’s merely a signal.”

What’s interesting about this statement is that it applies to both organisations and individuals.

We often see this in many organisations: it’s always the problems they can see or surface that are assumed to need attention, which are solved. However, it’s the underlying structural condition that keeps coming back as a new version of the same symptom. The problem they see explicitly is merely a signal, and solving it does not eliminate the problem completely, since only the signal is addressed. People can blame it on the system, management, or a person and hide behind the symptom, creating the problem. At any time, organisations can hire external consultants to address the symptoms.

However, for you as an individual, there is no such escape. You are both the doctor and the patient. You are the system that’s simultaneously generating the symptoms. Also, you are the observer trying to read them, and if there’s somebody who has the most to lose, it will be you, assuming your diagnosis is inaccurate. Also, remember, there is a total conflict of interest. Hence, individual misreading of signals is widespread and often more damaging than organisational misreading.

Robert Kegan and Lisa Lahey at Harvard conducted in-depth research on this topic, and their findings are quite interesting. They concluded that individuals have “hidden competing commitments” - where their stated goals contradict their unconscious goals! As individuals, it is very hard for them to see competing commitments because seeing them would require confronting a "big assumption" about themselves.

Differentiating the signal, the problem and the symptom.

Here is a day-to-day example that may happen at your workplace for you to think and understand this a little more deeply:

• You may have identified a problem like: "My problem is that I need to speak up more in leadership meetings."

• You have identified the problem correctly. You may also be taking action on it by reading books, attending some training programs, etc.

• But lying beneath could be another goal: "I am committed to never appearing incompetent in front of senior leaders." And this may be primarily driven by the assumption that"If I am seen as incompetent, I am fundamentally unsafe — professionally and personally."

• This underlying primary assumption operates at full swing in every leadership meeting inside you. It produces your lack of willingness to speak in leadership meetings. Not speaking up is the signal, but fear of being seen as incompetent is the symptom.

• "I don't speak up enough" is the named problem, but "What if I am seen as incompetent?" is the actual symptom. If you address the fear of incompetence, your ability to speak will improve.

Carl Jung, Swiss psychiatrist, psychotherapist, and psychologist, defined this problem extremely well: “The qualities individuals most strongly and quickly criticise in others are often the qualities they most forcefully deny in themselves.” The projection is the signal. Carl Jung’s insight was very insightful: “What you cannot see in yourself, you will see in others.”

As an individual, how do you misread the signals?

1. Seeing it as someone else’s fault:

   1. "My organisation doesn't value strategic thinking."

   2. “ He is a difficult person to work with.”

   3. “My team is not good enough”

   4. “My boss does not appreciate me.”

2. Naming the recent cause, not the underlying condition:

   1. "I'm burned out because this quarter was too intense"

   2. “The project got delayed because the client changed the requirements last minute.”

3. Naming the symptom that's visible, not the one that's actionable.

   1. "I don't have enough time"

   2. “Too many changes in priorities and changes to work on.”

   3. “Don’t have enough resources or bandwidth”

How do you learn to address the symptom and not the signal?

If the problem keeps recurring, you can rest assured that you're addressing the signal, not the symptom.

Here are some simple tips and suggestions that you can consider:

1. Treat your recurring criticisms of others as diagnostic data about yourself: If you are consistently emotionally charged and criticising others in a specific domain, it is worth interrogating yourself.

2. Treat the problem as a hypothesis and seek evidence: When you see or identify a problem, don’t come to a conclusion immediately. Try to generate two or three alternative explanations and consider the evidence that explains the closest reasons as to why the problem is happening. Actively look for the evidence to confirm your problem definition.

3. Practice time-delayed reflection: Maintain a diary in which you record the problems you identify and when you solved them. Revisit this once a quarter or in six months and see if you are addressing a similar problem with a new variation. Then it tells you that you have not solved the symptom, but only the apparent signal.

Remember, when organisations misname problems and address the apparent signals, they try to protect power structures. An organisation can be restructured. However, when individuals misname problems and constantly focus on the apparent signals, they tend to protect their self-image. This mistake can be existential and can hamper their growth. This therefore needs to be examined and questioned voluntarily.

It’s also time for you to think about this a bit.

Domain Expertise Has Always Been The Real Moat.

Aaron Brethorst writes in this article, “The hard part of writing software has never been the writing. It was building a working model of the domain in your head first.”

This is the cardinal truth that has always been spoken and understood by many people. If you do a root cause analysis of software projects as to why they succeeded or failed, or were delivered on time or delayed, or had near-zero bugs or continuous fixing, a good balance of domain knowledge and coding expertise would be one key difference that will keep coming up over and over again.

However, AI has bridged the gap in code development, and it is now up to developers to assess whether the code is right. And this requires a different level of judgement, not just code-writing skills. And this transformation is changing the profession itself.

Aaron highlights some key points to accentuate this transformation and who are the ones who are poised to succeed:

• “The first is a domain expert with no real software background. Hand them an agent, and they are startlingly effective, because the thing they’re missing, the ability to produce code, is exactly the thing the agent supplies. What they bring is the thing the agent can’t: the ground truth.”

• “The second is a strong generalist engineer who has never worked in the domain. They can architect anything, they know reliability and testing and how to keep a system from falling over at 2am. They can verify that the software is well-built. They cannot verify that it’s correct, because correctness here is defined entirely by a domain they don’t hold in their head.”

• So the most valuable person in this new world will be the one who has both skills because they can verify at both layers. They know the generated code is sound and they know the answers it produces are true.

You can read the entire article here.

How To Build An AI-Ready Culture: A Practical Guide.

In this AI Daily Brief podcast, Nufar Gaspar, Head of Research, Superintelligent, discusses how culture is critical to AI adoption in any enterprise. She presents a CHANGE framework for building an AI culture.

What are the individual components of the CHANGE Framework?

• C- Communication

• H - Human Oversight

• A - Attitude

• N - Network

• G- Governance

• E- Enablement

Nufar explains the various aspects of this framework extremely well and how to put it to work in a company.

You can click on the above link and listen to this conversation.

Stanford Leadership Forum 2026: Rewiring The Workforce In The Age of AI.

At the Stanford Leadership Forum 2026, Paul Oyer, Tamay Besiroglu, Susan Athey, and Nela Richardson share their perspectives on how AI is reshaping and rewiring the workforce of the future.

Here are some interesting insights from these conversations:

• Tamay: “I think I don’t expect a very drastic amount of acceleration in automation over the next one to five years. I expect that in the next one to three decades, we’ll probably have very widespread automation.”

• Susan: “We can have great technological improvement, but that improvement only translates into impact when it’s combined with an existing system or an existing organization.”

• Nela: “Where the jobs are coming and where the tech is going is totally different. It's upskilling those young workers to more complex tasks.”

• Susan: “If you have an idea about how to conceptualize and solve a problem and break it down and measure and test, then you have these tools that can make that idea a reality.”

• Susan: “AI-powered products delivered to small business owners through their mobile phones.”

• Nela: “AI is destroying knowledge work. We have to change our measurement of productivity, we have to change our measurement of engagement.”

Don’t Be An Economic Professional. Adapt To Become A Living Professional.

Thinking through ideas shared by Aaron Brethorst and Stanford Leadership Forum’s discussions on rewiring the workforce, and Nufar Gasper’s thoughts on building an AI-ready culture in companies, one common theme that comes through in all three is that it’s the professionals themselves who are experiencing this volatility and churn. Companies make their call on layoffs and downsizing when disruption or volatility hits their business. Some of these decisions are made in a couple of quarters.

It’s the people who work in these companies who are affected or caught by surprise. A great performer over many years is now not needed. The ones who were middle-of-the-curve performers, who stayed the course, assuming things would get better or kept chugging along, are the most affected. As things happen to them personally, it is a matter of their economic survival to be adaptable and stay relevant to take advantage of emerging opportunities amid these changes. If you are a professional working in such an environment, it is important to view the problem from a distance and work on a method to prepare yourself for these changes.

Today, professionals are managed as economic entities.

Most professionals are managed by employers as economic entities with a set of skill inventory, and their value is measured by the present market rate. The employer’s purpose is to maximise their revenue from their capabilities. When the market shifts, the economic professional is in crisis. Their identity is their role, and when that capability or role is disrupted, their need is threatened. Most professionals try to protect or extract the most possible from their old skills rather than acquire new ones. They defend their domain rather than expand beyond it.

Organisations you work for may have missed opportunities to identify market or technology shifts in their industry. But the ones who bear the brunt of this disruption are the professionals working in these organisations. They are caught unawares by the speed of change required in how they operate or reskill, and suddenly their need and relevance are in question.

Most professionals decline in their careers that span decades because they forget that the true essence of success lies in believing in a learning system — not a skill inventory. The economic professional exists to deploy their skills - it’s a worker’s mindset: “I will do what I am told repetitively well. Period.” The living professional exists to continuously develop their potential.

It may be worthwhile for you to think more deeply about what type of professional you are today.

So, what differentiates the economic professional from a living professional?

1. The economic professional is overleveraged financially due to their lifestyle needs relative to their current income. They get trapped by their cost base. It’s pretty similar to companies stuck with high expenses or costs. The living professional maintains or creates sufficient financial runway to experiment, retrain, spend a year learning something adjacent, or walk away from a role that has become a dead end. It in no way indicates that frugality is a virtue for them. Rather, they place a greater emphasis on their freedom as a strategy.

2. The economic professionals are involved in or consumed by a constant stream of shallow, reactive tasks that deplete their higher cognitive potential, which decays over time, thereby reducing their ability to adapt. The living professionals do not spend all their deepest cognitive capacity on low-value work but find genuine reflective time and the slack to think about what is changing in their environment before it becomes a crisis.

3. The living professional tries to maintain a deliberate peripheral vision. They read systematically outside their domain. They maintain relationships with people in adjacent fields. They pay attention to what is becoming true before the data confirms it. The economic professional’s expertise is real but isolated. When their domain is disrupted, they have no adjacent capabilities and no social network to carry them across.

4. The economic professional identity is mostly in domain terms: "I am a software engineer." "I am a marketer." "I am a banker." This identity is contingent on the domain remaining valuable. When the domain shifts — and eventually every domain shifts — the identity is under threat, and the professional resists the change rather than navigating it. The living professional identity is mostly in terms of values: “I am a person who applies rigorous analytical thinking to complex, ambiguous problems.”, “I am someone who builds trust with people and translates that trust into coordinated action”, “I am a person who synthesises across fields to find non-obvious connections.” Their identities survive every industry disruption because they are not contingent on any industry or technology.

5. The economic professional treats their current skills as their core asset. Skills are the balance sheet. Income is the return on those assets. When skills become obsolete, the economic professional faces a write-down on the balance sheet. The living professional treats their capacity to learn as the core asset. Current skills are instruments of that capacity — means to an end, not the end itself.

Living professionals and how they endured disruptions.

• Peter Drucker: He never defined himself as a management consultant. He defined himself as "an observer who explains how organisations and societies actually function." This identity allowed him to remain compellingly relevant from the 1930s to his death in 2005. He wrote his last book at 93.

• Charlie Munger: He never defined himself as an investor. He defined himself as "a learning machine that builds a latticework of mental models." This identity made him infinitely durable. This also defined his specific investment strategies and tactics.

• Leonardo da Vinci: He never defined himself in one category. He defined himself as somebody with “rigorous observation and first-principles investigation of how things work.” He applied it to anatomy, engineering, botany, and art. The domain was always secondary.

So how do you begin your path to becoming a living professional?

1. Deliberately pursue margin activities like learning, reading, building, experimenting in areas adjacent to or outside your current domain. These will matter in five years, so you must begin developing them early. These may not give you immediate returns but will matter when your core domain gets disrupted.

2. Don’t build a rigid five-year career plan with a fixed mindset and growth trajectory bias. Just maintain directional clarity (your identity, your values, your methods) while allowing tactical flexibility in how these can be applied.

3. Ask yourself this one question: “If I had to restart in a completely different domain tomorrow, what would I bring that would still be valuable?”

4. Build a diverse network of connections: It’s not enough to have a network of people in your domain who validate your current expertise, but you will need a diverse community of thinkers and practitioners across domains whose intellectual curiosity keeps you energised, uncomfortable and on the edge.

Therefore, as a professional don’t optimize just for your current output. Work on capabilities that allow you to continue to grow across a forty-year career spanning multiple industry disruptions, multiple technological discontinuities, and multiple transformations of knowledge domains that markets will value.

Work to transform yourself into a living professional who compounds in value, judgment, and contribution rather than peaking at thirty-five and then defending the domain knowledge that you once had.

The YouTube Revolution In Knowledge Transfer.

Samo Burja, a sociologist and the founder of Bismarck, a firm that analyses institutions, from governments to companies, offers a very interesting perspective on tacit knowledge. In this article, he writes that YouTube may be the largest platform for transferring tacit knowledge at scale. He analyses the reasons why tacit knowledge transfer through YouTube is revolutionary, due to the availability and access to cheap digital cameras, mass broadband, search engines, and portable screens.

Here are some powerful thoughts that Samo Burja has shared in this article:

• "If a master woodworker fails to transmit his tacit knowledge to the few apprentices in his shop, the knowledge is lost forever, even if he's written books about it." Documentation is not a substitute for transmission.

• "Through these videos, learners can now partially replicate the master-apprentice relationship, opening up skill domains and economic niches that were previously cordoned off by personal access."

• "Who today would argue that YouTube will one day be evaluated in the same category as the printing press or the telegraph? It is quite possible it will be."

You can read the entire article here.

Regain Control of Your Focus And Attention with Researcher Gloria Mark.

Gloria Mark is a professor of informatics at the University of California, Irvine and a visiting researcher at Microsoft, and the author of the new book Attention Span: A Groundbreaking Way to Restore Balance, Happiness and Productivity. She offers research-backed tips on how to regain control of your attention and help your teams do the same.

Here’s the summary of some ideas she suggests to regain focus and attention:

• Raise your meta-awareness and ask yourself, “Do I need to think, behave and act the way I did right now?”

• Practice forethought - Imagining how current actions affect future state.

• Design your day - Don’t pack your day with back-to-back meetings and design for ‘empty spaces’ in your day.

• Organisation can create ‘quiet time points’ during the day.

Understanding Complexity | Dave Snowden

Dave Snowden, founder and Chief Scientific Officer of The Cynefin Company, is in conversation with Garin & Dani from Distinction Consulting. He argues that in complex systems — which all organisations are — the instinct to plan, control, and cascade goals is not just ineffective but structurally wrong!

Here are some very interesting contrarian viewpoints that he puts forth in this chat:

• Complex and complicated are not synonyms — and treating one as the other is the root cause of most organisational failure.

• A leader's job is not to determine outcomes but to change the conditions in which interactions happen, then observe and amplify what emerges.

• Dave says that the entire movement toward defined ideal behaviour — "you need an agile mindset", "you need psychological safety" — is manipulative. The right approach: create processes that naturally generate the behaviour, without making moral judgements about individuals.

• Dave also makes a very powerful and provocative statement which is worth thinking about and reflecting on: “AI is reducing human intelligence to text. You can write down less than 10% of what you know anyway. Focus on text and tokens is problematic.”

• Decision-making degrades above seven people!

Why Is There An Attention Deficit In Organisations?

It is not uncommon for people to ignore an email, pay insufficient attention to the communication they receive, or forget the key points discussed in a meeting. You expect a response, but it never comes back. What’s surprising is that it is not just junior or middle-management folks who display this behaviour, even senior management folks behave similarly. Some information and responses are ignored consciously. The majority of others are missed till they are reminded. Sometimes, it is couched under the label of immediate priorities, not having enough time, being busy, paucity of resources, not having enough data or plans, etc., but the real truth is that enough attention was not paid to them.

How can you spot poor attention?

Today, most organisations generate more information than their people can attend to. Human attention is finite. It cannot scale with the supply of information. When the information supply grows faster than the capacity to focus, the surplus information does not get processed. How, then, do people decide which information to pay attention to?

1. Some people decide what they must read and what they must skip, and this is not based on strategic importance but on what arrives first, what is loudest, and what comes from people with authority.

2. Some people feign attention. These people sit in the meeting, open the details, browse through the dashboards, notes, or presentations, and retain almost none of it. It’s their appearance of being informed that substitutes for the reality. This is the most dangerous and a clear signal for failure, as this behaviour is virtually undetectable. In your eyes, everyone looks engaged, but nobody is integrating and processing information from the many sources they may be receiving.

3. There is a virtual breakdown of signal-to-noise ratio. It is not rare to see that attention, when fragmented across many pieces of information, produces even less understanding than when the focus and concentration were on two. An organisation with, say, twenty reports or dashboards is less informed than one with two - not because the data is worse, but because no single person has the attentional bandwidth to read, collate, integrate and process it.

The best way to understand this is with an example. It’s like a library with 10 million books. This is not useful to a person with ten thousand books — if the person is a reader who has one lifetime, no index, and no librarian. Adding books beyond the reader's capacity to find and read them does not increase knowledge. Today’s organisation generates a lot of internal information, much like a large library built with the lowest probability that people will access the relevant books and the most uninterested readers!

How do you overcome attention deficit in your organisation?

It begins by understanding, appreciating, and acknowledging that human attention is the scarcest resource. Most organisations are optimised for responsiveness and treat availability as a proxy for productivity. The professional who is always responsive, even if their output is shallow and frequent, appears more present. Over time, communication has been made frictionless in organisations and relatively cost-free - what started with email is now extended to Slack, WhatsApp, Teams, Zoom, etc. The cost of all this has been shifted to the receiver, who has to manage context switching, multitasking, and sudden interruptions. This is the primary structural cause of attention deficit at organisational scale.

If you are heading an organisation, or even running a team, or a group of team members, department, or function, you must find ways to determine how information, decisions, or discussions are surfaced to whom, at what frequency, in what form, and what will not bubble up to them.

Here are some steps and processes to reduce attention deficit:

• Learn to leverage the power of asynchronous and synchronous ways of working: The asynchronous way of working respects that not all communication between people or teams requires an immediate response or a meeting. Responses can wait for an agreed number of hours - say 8 hrs, for instance. It saves time and increases the depth of attention and productivity of people working on their planned work. It allows them to plan in advance and think ahead before responding to communications or meeting requests. There is no anxiety syndrome that they have not responded, even if it’s a person of authority or seniority. The culture is one of asynchronous response by default and synchronous response by exception.

• Make meetings attention-designed: It’s well known that Jeff Bezos hated PowerPoint presentations. Amazon started the practice of a six-page narrative memo read in silence at the start of every meeting. The memo forced the person to think through the problem completely before the meeting. The silent reading forced the meeting's constituents to focus on the problem under discussion. Hence, these became decision-making meetings rather than just information-sharing sessions.

• Redesign and Reprogram Calendars: Nobody stopped calendars from having fifteen-minute or half-hour breaks after meetings rather than back-to-back meetings. You can program calendars with half-hour meetings with a 15-minute break, or 1-hour meetings with a half-hour break before the next meeting. It allows people to think and reflect on next action points. Also, block off some parts of the weekdays- say, 1 hour on Wednesdays or Thursdays in the calendar as no-meeting hours for everybody. It puts pressure on people to think rather than blame the system. It improves attention span multi-fold.

• Resist your urge to be an ‘always-on’ or ‘meet now’ leader, manager, or employee: If you send or respond to messages at unearthly hours, it signals to the people around you to be hyper-responsive, in the hope that they will be rewarded. Think before you respond or send messages, whether it is critical or can wait a few hours or until the next day. Similarly, many leaders are known to call for unscheduled or unplanned meetings. It puts the people working with them in a tizzy, as they drop what they are doing to attend this meeting. Think again before you call for these meetings. Unless critical, try to avoid it. Be ruthless about others' non-responsiveness, too, when you create this attention-intense ecosystem within the organisation. This often gets missed and not handled firmly, as people assume late response as an unwritten licence or policy.

• Practice the art of small-group meeting invites or communication: One of the biggest attention-deficit catalysts in organisations is seeing meeting invites or communication to a very large number of people. Every other person assumes they will not attend that meeting or respond when they see such an invitation or communication, because they can get a debrief from someone else. Also, no decisions are made when there are a large number of people in these meetings or communications. It is a huge distraction to be CC’d on every email thread or meeting request. Communicate or meet with people who only matter. Let information sharing happen asynchronously. Tightly delegate the authority to decide to a very small number of people. It drives accountability and attention to get things done.

To improve attention deficit in organisations or in your teams, move from rewarding visible effort to rewarding demonstrated impact. Be comfortable playing with unfilled yet purposeful calendar space rather than seemingly productive, squeezed calendar slots. Be ready to transform into a professional who explicitly values ‘deep and intentional’ work rather than encouraging responsiveness as a marker for performance.

State of Curiosity Report 2026: Research On What’s Cultivating And Crushing Innovation in Workplaces.

A report recently released by SurveyMonkey shows that organisations are not short of curious people — they are short of cultures that let curiosity survive.

Here are some key insights from this report:

• Curiosity is often defined in academic literature as the intrinsic pull to close an information gap.

• “Once you get an answer from AI, it feels as if it’s complete and there’s nothing more to know.” Jack Soll is a distinguished Professor of Management and Organisations at Duke’s Fuqua School of Business.

• What is Curiosity capacity? It’s the practiced ability to stay open, ask sharper questions, and keep learning alongside AI.

• The three forces draining curiosity capacity are: the AI middleman, the scroll reflex (accept whatever the model produces as-is, or do a quick check and move on), and the efficiency squeeze (the time curiosity needs to notice what’s missing, find someone who might know, come back with a better question — has been engineered out of the workday)

Read the entire report here.

Overconfidence And The Art Of Knowing Yourself.

In this podcast, Brené Brown, research professor at the University of Houston, is in conversation with Adam Grant, Professor, Organisational Psychologist, and Author, in The Curiosity Workshop Podcast. The discussion provides a powerful perspective on the meaning and value of metacognition and how it affects the way you think, behave, and act.

Here are some key points from the conversation:

• Metacognition is thinking about your thinking. It’s the ability to notice your own thought processes as they happen and intervene before they become decisions.

• The Dunning-Kruger effect is not just about ignorance. The research also found that the least competent people in a domain consistently overestimate their ability.

• When competence gets miscalibrated, it has compounding consequences. And the conversation tries to answer an important question: “What happens when your confidence outruns your competence?”

• One key and most important takeaway from this conversation is that the most dangerous person in the room is the one who doesn’t know what they don’t know — and has never learned to ask. When you adopt and embrace the process of metacognition, it helps you discover and realise more about what you know and what you don’t know.

Ian Leslie - Understanding Curiosity

In this conversation, NYT bestselling author, Substacker, and cultural adviser Ian Leslie talks to Steve Berg, host of the Intangibles Podcast, about curiosity. Ian Leslie highlights how curiosity is correlated with intelligence and how we can learn to be more curious.

Here are some lovely takeaways from this conversation:

Why The Moat Of The Next Decade Is Not Artificial Intelligence.

It’s the ‘how’ and ‘what’ that’s built on top of it.

The moat of the next decade is not going to be AI — it is going to be organisations or firms populated with curious professionals. A curiosity culture emerges when professionals understand that they know little and sense there is more. The problem with AI is that it gives professionals instant top-line answers without ever building the underlying deeper knowledge. History has taught us these lessons, and it’s worthwhile to look back and learn from them.

How patterns repeat every 40 years. And we are in that moment now.

In 1913, Ford’s moving assembly line was one such mass-manufacturing idea, and soon competitors started copying the model. Ford’s moat was not the moving conveyor belt but the operating philosophy that made it a leader in the automotive industry at the time. The steam engine became standard. The moat moved to the assembly system.

In 1994, Amazon launched with the same internet technology available to every other retailer, but it was its logistics architecture, customer data flywheel, and management operating system that turned information into decisions faster than incumbents could respond. The internet became standard. The moat moved to the logistics and data flywheel.

Now, AI is becoming standard. The moat will migrate again. Harvard Professor Clayton Christensen identified the mechanism precisely - once a technology matures and standardises, competitive advantage migrates downstream — to whoever controls the performance-defining subsystem. It’s only the organisations and professionals who build explicit processes to convert their tacit knowledge into institutional or professional knowledge who will develop competitive advantages that tools cannot replicate.

Why AI cannot be the moat?

Sure, AI creates value across every domain and business function, but when you apply the lens of whether there are no alternatives, the answer is no because there are multiple models and they will multiply every quarter. Can clones or imitations be built? Again, the answer is a clear yes, as DeepSeek was replicated cheaply. Therefore, any AI technology or platform becomes substitutable. Electricity became the standard, and the moat moved to many industries, where many products or services were created using the standard innovatively and productively.

Why Curious Professionals will be the moat?

AI synthesizes from existing knowledge. AI is, in fact, accelerating the curiosity deficit rather than solving it. However, curious professionals will operate at the boundary, generating questions that push it further. This will become the primary source of competitive differentiation in an AI-enabled world. They build curiosity through the deliberate practice of asking questions and considering previously unimagined applications, which helps foster innovation atop existing AI models. This cannot be transferred or created when you hire a new team or license a new platform.

Therefore, organisations that will succeed through the 2030s are not those that buy or use the best AI models, but those that foster a curiosity culture.

How should organisations build a curiosity culture?

A culture of curiosity encourages people working there to be comfortable saying, “I don’t know.” CXOs must be comfortable with accepting their own not-knowing in public - in meetings, in townhalls, with team members. However, most organisations encourage leaders and managers who project confidence as if they know it all, when in reality the opposite is true. Today’s culture rarely pulls people up for complacency, which sets in across levels over time, but rewards them for not asking questions, for blind loyalty, and for working within a leader’s norms. A culture of curiosity must disincentivise people with a complacent attitude and remove the fear of not knowing or of failure. A curiosity culture will have to start not only measuring what was produced but also what was discovered or invented. Also, there must be a fair mix of status update meetings and more inquiry-led meetings, where curiosity-led thinking comes into play with people and teams in organisations.

How do you develop to become a curious professional?

It is not enough if organisations create an environment for a curiosity culture. It's the people who work there who need to change, too. Too often, professionals in companies equate effort or output with high competence and demand rewards for it every year, while in reality, there is ‘competence decay’ as they grow.

Competence and the ability to assess your own competence are two separate skills. You must build the feedback loop deliberately, because it will not form on its own. You must build an honest signal about the gap between your self-assessment of your competence and reality.

• Practice Calibration: Before any project or decision, write down your confidence level as a percentage. Then track outcomes. Over time, patterns emerge — domains where you are reliably overconfident, domains where you are weak or undersell yourself.

• Do a Premortem: You are always used to doing post-mortems once something succeeds or fails. Try to do pre-mortems before doing any work or solving a problem that has been assigned to you: ask yourself one question —assume this project or work has already failed—what killed it? This forces your brain out of planning mode into diagnostic mode. It surfaces the risks that optimism suppresses.

• Start Teaching: You cannot teach what you do not understand. The moment you attempt to explain a concept to someone unfamiliar with it, every gap in your knowledge becomes visible.

• Seek disconfirming evidence as a discipline: Normally, your default thinking mode is to seek information that confirms your current belief. Ask yourself one uncomfortable question: "Why am I right?"

• Build a group of honest dissenters: Create a small group of people whose explicit role is to tell you what others won't. They are a group of people who can share with you uncomfortable truths about your strategies, skills, execution, and decisions.

  Organisations will need to structurally incentivise a curiosity-driven culture to build their moat, rather than assuming it will arrive with platforms or tools they will license, or from the talent they have or will hire.

As AI becomes the standard, curiosity will increasingly become a strategic capability, specifically one that will distinguish the best professionals and organisations from the average.

How to Start a Career When AI Is Doing Your Entry-Level Job.

Katie Parrott is a staff writer at Every, and in this article, she provides valuable advice and insights on how to approach your career if you are just starting out in the age of AI. In fact, the advice is so practical and relevant that anyone, whether you are beginning your career or at the mid- or senior level, can take many takeaways to apply them in your workplace.

Here are some thoughts and recommendations she makes for you to think and reflect on:

• Chase problems, not professions: “The role of “content marketer” or “data analyst” may shrink, split, or even vanish, but the problem behind those titles—how to get a stranger to pay attention to something they didn’t know they cared about, how to make sense of a pile of messy numbers—will still be there, and somebody will still be paid to solve it.”

• Choose one discipline to protect: “Once you’ve picked your problem, pick your craft, whether it’s writing, building, researching, designing, strategizing, or operating. You don’t get any good at anything until you’ve done it many, many times.”

• Make things before anyone asks you to: “Pick the thing you’d want to use yourself, and make it. Once your work gets you in the door, the conversation that follows is going to be about how you made it.”

You can read the entire article here.

Building Robots To Get Kids Hooked On STEM Subjects.

In the Working Scientist Podcast series, Solomon King Benge, Founder & Executive Director, Fundi Robotics, highlights the importance of giving education a stronger practical focus and the imperative to move away from rote learning at a blackboard.

Here are some thoughts that he has shared in this conversation:

• What can permission-driven education do for learning and understanding science?

• Why does there have to be something a little bit more to education than just sitting in a classroom and memorising facts?

• Learning in school should be less about exams and more about actual, real-world solutions. This enhances kids' curiosity, interest, and understanding better than just a lecture.

• The importance of kids exploring and experimenting in learning and the curriculum.

• Teachers cannot enter the teaching profession just because it’s their last resort.

The Reason Your AI Rollout Isn't Working with Charlene Li.

Charlene Li, an author and strategic advisor, says in her conversation with David Burkus that most organisations treat AI as a software upgrade. They just buy the licenses, hand them to IT, measure adoption rates, and call it a transformation. It isn't. She makes a very strong point when the CEO outsources AI to the CIO, the whole organisation stays stuck in adoption mode. She says that in most organisations, these AI tools get installed. Therefore, behaviour doesn't change, and value doesn't follow.

Here are some points that she makes on how we can leverage AI:

• “AI can emulate empathy, but it's not the same(as humans). So if AI can do the heavy lifting and spare us empathy fatigue so that we can reserve our empathy for the times

  and places where AI can’t do it.”

• “I look at AI as the ultimate translation tool, not in terms of languages, but in terms of perspectives.”

• “To normalize the use of AI, just begin a meeting by saying so how did you all prepare for this meeting with AI.”

• “Knowledge is a commodity now. What are we teaching in schools? We’re teaching, in reality, the ability to learn, to be curious, and to exercise good judgment and wisdom. What's your velocity of learning?”

• “How are we creating a workforce for the future that is going to be very flexible, knowing that your job is probably going to change every 18 to 36 months?”

• “You have to direct AI to solve the right problem. So it forces you to think about things in a much more systematic way to look at things and to reframe things.”

• “Do not get distracted by the bright shiny object. Do not let a vendor begin the conversation with their pitch. Always begin the conversation by saying these are our problems.”

• “Leaders create change. Managers maintain the status quo.”

Why ‘What You Know’ Is No Longer Good.

What you can perceive and do with what you know is increasingly becoming more valuable.

As AI takes centre stage, the gap between people who simply know and those who know, understand, and have a perspective will widen and become clearly more visible. This difference, amongst people irrespective of their levels and years of experience, was not so stark in the industrial and knowledge-economy era, but it will become quite apparent as AI gets embedded in everything we do.

Let’s analyse how the importance of these three levers is changing today. And what makes this difference or gap among professionals strikingly apparent and dominant across the three phases of the Industrial (1760s-1960s), Knowledge/Digital(1960s–2020s), and AI (2020s to present) eras?

• Knowledge: Knowledge is nothing but a collection of facts, data and information. At one point, it was enough for people to simply remember, state, and describe what they knew. The professional markers of knowledge were just degrees, credentials and certifications. People could still survive by knowing some information, facts, etc., but understanding very little of it. Fragile knowledge was sufficient to get or do a job. The industrial era was an economy of physical execution. Consistently doing what is given to people was considered valuable. Largely, people were hired, rewarded and promoted for this capability during this era.

• Understanding: It's about grasping the "why" behind the information or data people know. It goes beyond data and facts to understand causality, relationships, etc. Understanding requires people to do thought-demanding things like thinking, explaining, finding evidence, generalising, analogising, applying the knowledge and using it in new ways. Therefore, the understanding was gained through years of application and practice, and it resided in professionals’ heads. The knowledge or digital era was an economy of specialisation. During this era, companies transformed from production organisations to service organisations. The ability to apply and use acquired knowledge was valuable. Expertise was scarce, and scarcity made people valuable. Most of the time, scarcity drove unrealistic premiums in what people earned or demanded.

• Perspective: This is not all about having both knowledge and understanding, but about having the ability to interpret and contextualise in the situation. As AI handles more and more routine tasks, people’s contribution will increasingly be perspective-centred - focusing on judgment under uncertainty, creativity, relationship-building, and through tacit knowledge that comes from experience. The AI era will be an economy of judgement and perspective. Therefore, people will only be able to add value when they apply knowledge and understanding, along with perspective, by evaluating, creating and applying the understanding to new and ambiguous situations. It will also become imperative to transfer learnings across domains. Knowledge and understanding will begin to collapse when a novel problem arises, and there’s no template to apply. They need to develop a perspective on what they know and understand.

So what does all this really mean to you?

Neither formal education nor your current work environment prepares you for these capabilities.

It is a hard truth that most of these capabilities and skills gaps are widening faster than most formal education and work environments can adapt. Therefore, many job roles are getting restructured, not just disappearing or getting replaced. They are getting redesigned. Most job descriptions are increasingly being rewritten. Over the last 100 years, it has always been assumed that anyone entering the workforce is a linear accumulator of knowledge. They start as juniors, move to a mid-career role, and grow into a senior role. These long-established organisational structures and career ladders are breaking down.

If AI is automating entry-level work, the assumption is that the first few years of grinding, repetitive work, and practice that helped people gain knowledge and understanding over time will no longer be available to this generation. Mid-career professionals who grew just by managing people, tasks, and work processes are being replaced by workflow agents. Senior-level professionals will have to bring their domain knowledge and genuine perspective to their roles and drive outcomes, not just output. With AI, they have to learn to lead, be hands-on, and still manage, do this concurrently. And they must learn to do it at scale, with agentic AI extending support with more hands and insights.

1. Therefore, if you are a professional who is knowledgeable and comfortable with doing or delivering repetitive tasks, going through the grind or being told what to do or executing without knowing, AI will outcompete you.

2. However, if you are a professional who understands and knows how to apply the knowledge you have gained, AI will help you amplify the work you do.

3. Finally, if you are a professional with a perspective and can share it with the organisation or the people you work with, you will be able to drive AI on your terms and become highly valued by your team and the organisation as a whole.

So, how do you prepare yourself for this change?

• Level 1- Stop being a ‘Task-driven’ Professional: Refuse to be the person who executes identified or given tasks within an established template. Having surface-level knowledge is not good enough, as AI will come with deeper knowledge, perform repetitive tasks better, and be more productive, as increasingly, knowledge is getting democratised by AI. If you don’t transform yourself, be ready to face declining compensation, lateral stagnation, and the wrath of headcount reduction.

• Level 2- Grow to become a ‘Hybrid’ Professional: You must expand your domain knowledge and combine it with a deeper understanding of the concepts and problems you are working on. You will see accelerated growth if you preserve and deepen your understanding layer. You must begin using AI as a tool to improve the quality of your work and enhance productivity and output velocity.

• Level 3- Elevate to become a ‘sense-making’ professional: This specifically can apply if you are or want to become a senior professional, as you will need to combine your deep domain understanding with a genuine perspective of the situation and context of the macro-environment in which you are working. You must be able to develop novel frameworks, derive meaning and make high-stakes judgements under ambiguity, and not just process information but also interpret it from others' perspectives. Such professionals will drive exponential value and get disproportionately rewarded.

What should you protect yourself from?

As you move from one level to the other, the accelerated use of AI in your daily work will restrict your ability to explore diverse viewpoints, develop a high probability of being unable to distinguish algorithmic content generated by AI and challenge some of the underlying assumptions it works with. You may feel more productive, but in reality, you may be losing your ability to think independently. It may lead to a situation where you feel you are seemingly producing more, but understanding less. You will end up going back to being a ‘task-driven’ professional as your ability to think critically reduces. You must be wary of this ‘understanding, thinking and perspective-centred degenerativeness’ that can set in very quickly.

Your ‘Knowledge-Understanding-Perspective’ loop needs to continuously spiral upwards as you embed more and more AI in your work.

Therefore, it will no longer be enough if you learn and know more. You must develop the ability to think more deliberately, reflect more deeply on what you know or learn, and think more independently. Just be aware, AI makes it easier for you not to do it!

How To Maintain Strong Friendships As You Move Through Your Career.

In the article in Kellogg Insights, Professor Neal Roese, a leading expert in the science of regret, writes about the importance of maintaining relationships throughout the course of your career. He writes that there will be location changes, organisational shifts and time constraints in your job as you grow, but keeping relationships does take effort and will not happen on its own. He provides some research-based recommendations, like:

• Put In the Effort: “One of the simplest ways to preserve a close friendship is to make a point of keeping it on your schedule.” It may be worth noting that there is a gender difference in maintaining connections. Women are better at maintaining one-to-one connections, while men need a nudge!

• Be Ambitious but Preserve What You Value: Taking time out with friends does not mean you have to limit your ambition or forgo opportunities. You have to go out of your way to preserve some friendships that you greatly value and want to maintain.

• Reach Out for the Perspective You Need: You must try to keep nurturing your close friendships over time. Your friends can often help you see your strengths more clearly, and therefore, they can help you in finding new ways to succeed. They can act as a great sounding board for you, too.

Read the entire article here.

Tamar Kushnir: Power of Imagination.

Tamar Kushnir, Professor of Psychology and Neuroscience at Duke University, is in conversation on the Stanford Psychology Podcast, discussing her research on imagination and answering the question, ‘Does everyone have imagination?’

Here are some key thoughts from Prof. Tamar on this topic:

Why Coding Is Solved And What Comes Next.

Boris Cherny, who is considered the father of Claude Code at Anthropic, joins Sequoia partner Lauren Reeder to discuss the future of software coding.

Here are some thoughts he shares in this conversation:

• What will teams look like in the future? “We’re going to start to see a lot more generalists who are cross-disciplinary….engineers who are really good at product engineering, but also really great at design, or really great at product and data science and engineering.”

• What’s changing with coding? “One that gets less important is switching costs... another one that gets less important is processing power…network effects,

  scale economies, cornered resources, things like that, these are not really

  changing with AI.”

• How will companies, competition and startups look like? “I think the number of startups in the next 10 years that are just going to disrupt everything is going to increase like 10x because right now, you can be a tiny startup, you could build a thing that’s as valuable as a large company, and you can actually compete head-to-head because the large company has to evolve their business process. They have to evolve the way they work. They have to retrain everyone to use technology. They’re going to face a lot

  of internal resistance to that.”

How Imagination Can Become Your Competitive Advantage.

Imagination requires you to challenge established norms, look at alternatives, connect the unconnected and question fundamental assumptions.

Children freely challenge what they see around them. Children, by nature, are curious, test many hypotheses and are comfortable starting from ignorance and looking foolish with their questions. Adults turn the same process upside down, as they gain more knowledge, apply richer cross-domain analogies, and intuitively recognise patterns from experience. Hence, most adults fall into an ‘Adult Trap’, where they come to rely too heavily on established precedents.

The companies that people work for only further this cause. In a majority of organisations or companies, they put the brake on their imagination, as most people settle into a culture of ‘fitting in and ‘going along’, and this is a death-knell for imagination and hence growth. Most high performers fill up every available gap in their calendar with calls, emails, and meetings, leaving no space to imagine, experiment, and play. People have ideas. They do not voice them. The real underlying contradiction is that the companies most people work for reward them for certainty. A majority of people, therefore, end up resisting uncertainty in their lives. However, uncertainty and unstructuredness are integral to developing your imagination skills.

Therefore, as an adult, over time, you begin to constantly defend the need to maintain or protect your reputation, or strive very hard to grow your position, or rank, and get unduly worried about creating cracks in relationships. This leads you to become overly cautious or dismiss any new patterns you discover in what you're pursuing.

Here are examples of some business models that have disrupted traditional assumptions, as the leaders of these companies questioned established ways of working and reimagined them:

• Amazon: Why does retail need a physical storefront? Businesses can afford to lose money on delivery but can earn on scale.

• Airbnb: Is there a need for hotels to necessarily invest in physical infrastructure and overheads? Is there a better way to optimise available or unused capacity? The hospitality business does not need infrastructure, it can leverage existing capacity. There was a segment waiting to lap up this need.

• Netflix: Why rent and replace DVDs? Why not stream directly?

• UPI(Universal Payment Interface): Is there a more, easier, and convenient way of transferring money between people? Why have bank accounts created walled gardens for transferring money?

However, imagination, by its nature, requires the ability to work with ambiguity, to test hypotheses with limited data, and to allow for unstructured time and thinking. The natural instinct for many of us is to seek clarity before imagining. At work, you must have noticed that many strategic decisions are rarely made with all the facts in hand. You must develop the ability to gain meaningful insights from what little is known. This requires two important skills that you must build on:

• Inductive Leap: Ability to jump from specific observations to broader, generalised conclusions. This is not simply blind guessing but rather exploration beyond what the data reveals, using pattern recognition, prior knowledge, and imagination.

• Epistemic Courage: The intellectual courage to express or voice your opinion about beliefs that are true, after accurately ascertaining facts or patterns, despite social, personal and professional costs that may come with it.

Imagination triggers the creative tension between what is observed, what is normally done, and possible new ways of thinking and solving a problem that may or may not exist. That’s possibly the moment for you to take an inductive leap, with an idea, when there is not enough data, but you sense some patterns emerging. If you don’t develop epistemic courage, the idea you come up with is retracted, diluted, or never voiced due to the fear of social resistance.

In fact, imagination is becoming more important than ever in the age of AI. The WEF Future of Jobs Report 2025, based on research across more than 55 countries, 1,000 leading employers, and 14 million workers, highlights the importance of this skill. AI is great at finding optimal solutions within boundaries using convergent thinking, while humans excel in breaking boundaries and making unexpected connections between seemingly unrelated ideas. In another landmark 2026 study of 100,000 humans, the research found that while AI can beat average human creativity, the most imaginative minds are still unmistakably human.

So, how do you work further and get better at imagination?

• Learn to ask questions that aren't yet framed. Imagination will be more centred on problem-finding rather than problem-solving, which AI will increasingly excel at.

• Imagine future states: Thinking about future states requires contextual understanding, which is a lot more emotional and an ability to connect the dots through divergent thinking, which AI lacks.

• Ability to make meaning from patterns: While AI will continue to make computation abundant, the most scarce resource will become the human who can imagine what to compute and interpret the meaning from the patterns that will be thrown up or emerge.

If you, as a professional, want to shape what comes next, you must deliberately cultivate imagination through uncertainty tolerance, set unstructured time for exploration, epistemic courage, and inductive leaps.