What If Yesterday's Success Stories Were Forbidden?
One Strange Question. A New Possibility.
An Indian MBA campus story, Sam Altman's challenge to conventional ambition, and a practical PO–AI Thinking Lab for discovering opportunities that don't yet have a name.
The Question That Started It All
Every generation grows up admiring the success stories of the generation before it.
Students study successful companies.
Business schools teach established management practices.
Young professionals seek prestigious careers.
Entrepreneurs examine proven business models.
There is considerable value in learning from experience.
But what happens when the world changes faster than our definitions of success?
What if the career everyone wants today was designed for yesterday's economy?
What if the next extraordinary business opportunity does not resemble any company we currently admire?
And what if AI could help us discover possibilities beyond established career and business models?
These questions become particularly interesting in light of a recent observation by Sam Altman.
The News That Inspired This Episode
In an October 29, 2026, Times of India report, Sam Altman was quoted criticizing the tendency of MBA students to pursue opportunities that had been fashionable years earlier.
Speaking in an interview with Stanford Graduate School of Business, according to the report, Altman encouraged ambitious people to explore new opportunities emerging from technological change.
His central concern was not that established careers have no value. It was that aspiring leaders may overlook emerging opportunities when they focus too heavily on familiar success stories.
The report also described Altman's view that AI could enable new companies and significantly change how work is performed.
Source: The Times of India — Sam Altman on MBA students and new opportunities
This reported observation offers a powerful starting point for lateral thinking.
But rather than merely agreeing or disagreeing with Altman, let's introduce a deliberate provocation.
What if yesterday's success stories were forbidden?
1. The Story: Aditi and the Placement Race
It was placement season at a prestigious Indian business school.
The campus was buzzing with excitement.
Students were preparing for interviews with consulting firms, investment banks, multinational corporations and established technology companies.
There were workshops on résumé building, mock interviews, aptitude tests and case-study preparation.
Everyone seemed to know what success looked like.
A prestigious employer.
An attractive salary.
An impressive job title.
A career path that others would recognize and admire.
Aditi, a final-year MBA student, was among the most promising candidates in her batch.
She had excellent academic results, strong communication skills and several internship experiences.
Yet she felt strangely uncertain.
The familiar question
One evening, Aditi opened ChatGPT.
She typed:
"What are the best career opportunities for an MBA graduate in the AI era?"
The response was impressive.
It presented career options, skill requirements, industry trends and recommended learning paths.
Consulting.
Product management.
Corporate strategy.
Business analytics.
Technology leadership.
Entrepreneurship.
The answer was sensible.
But Aditi noticed something.
Most of the recommendations resembled paths that previous generations of successful graduates had already followed.
She wondered:
If AI is changing the world so dramatically, why am I still planning my future by looking backward?
The professor's unusual assignment
The next morning, during an innovation class, Professor Rao introduced a challenge.
“Imagine,” he said, “that every successful career path and business model from the past five years has become unavailable.”
The students laughed.
“That would eliminate most of our placement choices,” someone replied.
“Exactly,” said the professor.
He wrote a statement on the board:
PO: You are forbidden to copy yesterday's successful careers or business models.
Aditi raised her hand.
“Then how do we know what to pursue?”
Professor Rao replied:
“Perhaps that is the question you should be asking.”
The classroom fell silent.
For the first time, the students were not being asked to choose among established opportunities.
They were being challenged to imagine opportunities that might not yet exist.
The discovery
Aditi returned to ChatGPT.
But this time, she did not ask for the best jobs.
She asked:
"What important problems are emerging because AI is changing how people live and work? Which of these problems might require entirely new professional roles, services or organizations?"
The conversation changed.
Instead of merely ranking careers, AI began helping her explore emerging needs.
Who would help small businesses evaluate AI-generated decisions?
Who would design trustworthy human–AI workflows?
Who would help local organizations adapt AI tools to their languages, customers and working practices?
Who would investigate failures when autonomous systems made mistakes?
Who would help professionals preserve accountability while delegating routine work to AI?
Aditi had not discovered a guaranteed career.
She had discovered something potentially more valuable.
A new way of searching for opportunities.
And it began with a strange question.
The characters, institution and classroom events in this story are fictional. They illustrate the PO thinking method and are not presented as a real incident.
2. The Hidden Assumption: Success Means Following a Successful Path
Many students and professionals make career decisions by studying people who have already succeeded.
This is understandable.
Successful examples provide evidence, inspiration and practical guidance.
But they can also influence what we consider possible.
Consider some familiar assumptions.
Assumption 1: Prestigious careers are necessarily the best careers.
A recognized job title may provide status, income and opportunity. But it does not automatically guarantee suitability, satisfaction or long-term resilience.
Assumption 2: Established industries offer the most attractive opportunities.
Mature industries may provide excellent careers. Yet emerging needs can also create opportunities that are not reflected in conventional placement lists.
Assumption 3: The most popular career choice is probably the safest.
Popularity does not establish future security. Economic conditions, technological capabilities and employer needs can change.
Assumption 4: A new opportunity must resemble an existing job.
Sometimes new technologies create combinations of responsibilities that do not fit established professional categories.
Assumption 5: AI should tell us which career to choose.
AI can organize information and compare options. But its more interesting contribution may be helping us discover possibilities we have not considered.
The challenge is not to reject existing career paths.
It is to avoid treating them as the complete universe of opportunity.
A better question
Instead of asking:
“Which successful career should I follow?”
Ask:
“What important problems are emerging, and what new capabilities might be needed to solve them?”
That shift prepares us for PO.
3. Understanding PO: The Escape Technique
Edward de Bono developed lateral-thinking techniques to help people move beyond established patterns.
One such technique is escape.
In escape provocation, we identify something normally taken for granted and temporarily remove it.
The purpose is not to argue that the assumption should disappear in reality.
The purpose is to discover what becomes visible when we imagine its absence.
For this episode, the assumption is:
Established success stories provide the natural starting point for future career and business decisions.
Now remove that assumption.
The PO provocation
PO: Yesterday's successful career paths and business models cannot be copied.
Imagine that for one week:
You cannot choose a career simply because it is prestigious.
You cannot pursue a business idea merely because another startup has succeeded with it.
You cannot use an established job title as your starting point.
You must begin with emerging problems, changing needs and unexplored possibilities.
What would you investigate?
Who would you talk to?
What would you ask AI?
Which skills would suddenly become relevant?
This is where deliberate movement begins.
The provocation is not a literal career restriction.
It is a temporary thinking experiment.
4. The PO–AI Thinking Lab
Our refined PO–AI Thinking Lab follows six stages:
FOCUS → PROVOKE → MOVE → GENERATE → JUDGE → ACCOMPLISH
The first stages draw on Edward de Bono's methods of provocation and movement. The integration with generative AI, critical evaluation and practical experimentation is the AIYING extension.
Let's apply the framework to Aditi's challenge.
Stage 1 — FOCUS: What Are We Really Trying to Achieve?
Aditi's initial question was:
"Which MBA career should I choose?"
But that question already assumes that the correct opportunity can be found among recognizable career categories.
A more fundamental question is:
“How can I build a meaningful, economically viable and adaptable professional future in a rapidly changing world?”
This is a broader and potentially more useful focus.
Questions for reflection
What does career success mean to me?
Is it primarily income, security, independence, learning, social contribution or some combination?
Which abilities do I genuinely enjoy developing?
What kinds of problems motivate me?
What constraints must I respect?
What opportunities might I overlook because they do not have familiar names?
Aditi's initial assumptions
She identifies four assumptions:
Her MBA qualification should lead to a conventional management role.
A prestigious employer is the best evidence of a good career decision.
New opportunities are too uncertain to investigate seriously.
AI is primarily useful for comparing existing career choices.
She does not need to reject these assumptions permanently.
She needs to examine them.
FOCUS outcome: Redefine the objective from selecting a prestigious job to discovering a sustainable professional direction.
Stage 2 — PROVOKE: Remove the Familiar Paths
Now Aditi introduces the escape provocation:
PO: I am forbidden to copy any successful career path or business model from the previous five years.
Imagine that conventional consulting, banking, product-management and startup models are unavailable as templates.
This does not mean that the underlying skills become useless.
Analytical thinking, financial literacy, communication, operations and leadership remain valuable.
But Aditi cannot simply imitate the familiar professional destination.
She must search for something different.
The immediate discomfort
How will she explain her choice to recruiters?
How will she estimate income?
How will she know whether an opportunity is real?
What skills should she acquire?
These concerns are legitimate.
But during the provocation stage, she temporarily postpones judgment.
The goal is to expand the space of possibilities before evaluating them.
PROVOKE outcome: Remove dependence on familiar success models as the starting point.
Stage 3 — MOVE: Discover What the Provocation Reveals
Provocation alone is not enough.
We must move from the unusual statement toward useful concepts.
Let's apply several movement approaches.
Movement A — Extract the principle
What principle might emerge from the prohibition on copying old success models?
Build your future around emerging problems, not merely established job titles.
This principle can guide practical exploration without requiring us to abandon conventional careers.
Movement B — Examine the positive aspects
What might be useful about temporarily ignoring familiar career templates?
Aditi may notice opportunities that placement rankings overlook.
She may explore combinations of business and technical capabilities.
She may discover organizations that need new kinds of services.
She may identify problems that established companies have not prioritized.
She may become more willing to test ideas before committing to a career direction.
Movement C — Explore consequences
If graduates stopped relying exclusively on familiar career categories, what might happen?
Some would begin studying emerging customer and organizational problems.
Others would explore new combinations of existing skills.
Some might discover opportunities within established companies rather than outside them.
Universities might introduce more problem-discovery projects alongside conventional placement preparation.
Employers might recruit for capabilities and outcomes rather than only established titles.
These are possibilities, not predictions.
Movement D — Identify differences
Compare two ways of exploring a career.
Career-first approach:
Choose a profession → Study its requirements → Acquire qualifications → Apply for roles.
Problem-first approach:
Identify an important problem → Understand who experiences it → Investigate possible solutions → Determine which capabilities are needed → Test a professional opportunity.
The two approaches can complement each other.
But the second may reveal directions that the first overlooks.
Movement E — Find useful circumstances
Where could this approach be especially valuable?
During technological transitions.
When industries face unfamiliar regulatory or operational challenges.
When organizations adopt new AI capabilities.
When local communities have unmet needs.
When professional roles combine disciplines that have traditionally been separate.
The movement insight
Aditi begins with an impossible restriction.
She arrives at a practical principle:
Don't merely search for a job title. Search for a valuable problem that your capabilities could help solve.
MOVE outcome: Shift from career imitation toward problem-led opportunity discovery.
5. Stage 4 — GENERATE: Five New Possibility Directions with AI
Now Aditi uses generative AI to expand the directions uncovered by PO.
The following examples are exploratory concepts, not verified job openings or forecasts.
Possibility 1 — Human–AI Workflow Designer
As organizations introduce AI into everyday work, they must decide which activities can be automated and which require human judgment.
A possible professional opportunity is helping organizations design workflows that combine human expertise with AI capabilities.
For example, a small business might use AI to prepare customer communications, but retain human approval for sensitive decisions.
A workflow designer could help identify tasks, responsibilities, controls and measures of success.
Skills to investigate: Process mapping, business analysis, AI literacy, change management and evaluation.
Question to test: Are organizations willing to pay for help redesigning specific workflows around AI?
Possibility 2 — AI Decision Accountability Specialist
AI-assisted decisions can create difficult questions.
Who checks the output?
Who approves consequential actions?
What evidence supports a recommendation?
How are errors detected and corrected?
Organizations may need people who understand both business decisions and AI governance.
Aditi could investigate opportunities related to decision oversight, documentation, escalation processes and accountability.
Skills to investigate: Risk management, responsible AI, governance, auditing and organizational processes.
Question to test: Which organizations have unresolved accountability problems in their current AI workflows?
Possibility 3 — Local-Language AI Service Innovator
India has enormous linguistic, cultural and organizational diversity.
A service that works well in one language or context may be difficult to use in another.
Aditi could explore AI-assisted services designed around specific local needs.
Examples might include supporting small businesses with multilingual customer communication or helping service organizations make information easier to understand.
Skills to investigate: Customer research, multilingual service design, usability testing and AI tool evaluation.
Question to test: What important communication difficulties remain unresolved in a specific local market?
Possibility 4 — AI Adoption Experiment Designer
Many organizations are interested in AI but uncertain about where it will produce meaningful benefits.
Instead of offering general AI training, a professional might help organizations design small, measurable adoption experiments.
For example:
Can an AI-assisted process reduce the time required to prepare routine reports without increasing errors?
Can a customer-service team use AI to improve response consistency while maintaining human oversight?
Skills to investigate: Experiment design, operational metrics, project management and process improvement.
Question to test: Would organizations value structured experiments more than generic AI demonstrations?
Possibility 5 — Emerging-Problem Discovery Consultant
Organizations often begin innovation by asking for new solutions.
But what if the more valuable service is helping them discover which problems deserve attention?
Aditi could explore a consulting model that combines customer interviews, PO provocations, AI-assisted analysis and small experiments.
The consultant would not begin with a technology recommendation.
The process would begin with the organization's unresolved difficulties.
Skills to investigate: Research methods, facilitation, lateral thinking, business modeling and validation.
Question to test: Would a client pay for a clearer problem definition before investing in a solution?
6. Five More Possibilities Worth Exploring
The first five possibilities are only starting points.
Aditi could ask AI to explore additional combinations:
AI Evaluation Coordinator: Helping organizations test whether AI systems perform reliably on real tasks.
Human–Agent Operations Planner: Designing responsibilities and oversight when AI agents participate in operational workflows.
AI Accessibility Service Designer: Exploring ways to make digital services more accessible to people with different needs.
Workforce Transition Facilitator: Helping teams identify how changing technology affects tasks, capabilities and learning requirements.
Community AI Opportunity Researcher: Investigating locally important problems that might benefit from appropriate AI-enabled services.
Again, these are conceptual directions. Their economic viability, market demand and appropriate qualifications must be independently established.
A critical distinction
AI can generate plausible descriptions of new opportunities.
It cannot establish that employers are hiring for them or that customers will pay for them merely by describing them confidently.
An AI-generated opportunity is a hypothesis until evidence supports it.
7. Stage 5 — JUDGE: Which Possibilities Deserve Serious Attention?
This is where enthusiasm meets evidence.
Aditi should not abandon a credible job opportunity because an AI-generated career concept sounds exciting.
She needs a disciplined evaluation process.
Consider five criteria.
1. Meaningful demand
Who experiences the problem?
How frequently?
How costly or consequential is it?
What are they doing about it today?
2. Personal fit
Does the opportunity match Aditi's strengths and interests?
Can she acquire the necessary capabilities?
Would she enjoy the daily work?
3. Feasibility
Can she enter the field without unreasonable financial or professional risk?
Are there accessible organizations, customers or projects where she can gain experience?
4. Evidence
Are there real job descriptions, paid projects, customer interviews or documented organizational needs supporting the opportunity?
What remains speculation?
5. Responsible implementation
Does the opportunity involve sensitive information, high-stakes decisions or regulatory obligations?
What expertise and safeguards would be required?
Illustrative evaluation matrix
| Possibility | Main attraction | Key uncertainty | First evidence to collect |
|---|
| Human–AI Workflow Designer | Combines business and AI skills | Will organizations fund redesign work? | Interviews with operations managers |
| AI Decision Accountability Specialist | Addresses governance needs | Required expertise and qualifications | Real role descriptions and practitioner interviews |
| Local-Language AI Service Innovator | Addresses context-specific needs | Will users adopt and pay? | Customer interviews and prototype testing |
| AI Adoption Experiment Designer | Produces measurable business outcomes | Is there demand for facilitated pilots? | Discussions with small-business owners |
| Emerging-Problem Discovery Consultant | Differentiated innovation approach | Will clients pay for discovery? | One small paid or pilot engagement |
The matrix is a framework for investigation, not a ranking based on market research.
Aditi's decision
She decides not to choose a new career immediately.
Instead, she selects two possibilities for further investigation.
She will compare them with her conventional career options using real evidence.
This preserves both creativity and prudence.
JUDGE outcome: Select promising opportunities for testing without confusing novelty with viability.
8. Stage 6 — ACCOMPLISH: The Seven-Day Opportunity Discovery Challenge
The objective is simple:
Discover and test one promising opportunity that you would not have considered through conventional career planning alone.
This exercise is useful for students, professionals considering a transition, educators and aspiring entrepreneurs.
Day 1 — Map Your Conventional Choices
List your five most obvious career or business options.
For each, record why it appeals to you.
Is it compensation?
Prestige?
Security?
Interest?
Family expectations?
A successful role model?
Then identify which assumptions shape your choices.
Day 2 — Apply the PO Escape
Temporarily remove your five familiar options.
Ask:
“If none of these paths existed, what important problems would I want to help solve?”
Do not evaluate the ideas yet.
Use AI to expand your thinking.
Day 3 — Discover Emerging Problems
Investigate problems in three domains that interest you.
Look for credible evidence of changing needs.
Speak with practitioners.
Read relevant industry material.
Observe workflows.
Use AI to organize findings and propose questions, but verify factual claims independently.
Day 4 — Generate Alternative Opportunities
Ask AI to develop ten different professional or entrepreneurial responses to the problems you identified.
Avoid merely renaming familiar jobs.
Explore different delivery models, customers, combinations of skills and forms of value.
Day 5 — Evaluate the Evidence
Shortlist three possibilities.
For each, identify:
Do not confuse AI-generated explanations with market validation.
Day 6 — Test One Possibility
Conduct a small experiment.
Possible experiments include interviewing a potential customer, discussing the concept with a professional, developing a simple demonstration or offering to solve one narrowly defined problem.
The experiment should be inexpensive, ethical and reversible.
Day 7 — Decide What to Learn Next
Review what you discovered.
Did you identify a meaningful problem?
Did someone confirm that the problem matters?
Could you contribute value?
What skills would you need?
What evidence would justify investing more time?
You may decide to pursue the opportunity, investigate further or abandon it.
All three can be useful outcomes.
The real measure of success
The challenge is not to invent a fashionable new job title.
It is to discover an opportunity that is:
Meaningful enough to matter. Plausible enough to investigate. Valuable enough to test.
9. The Complete ChatGPT PO Prompt
Here is the full reusable prompt for students, professionals and entrepreneurs.
Copy this prompt into ChatGPT
Act as my PO lateral-thinking partner, using Edward de Bono's provocation and movement methods.
My challenge is to discover new career, business or entrepreneurial possibilities in the AI era.
Use this provocation:
“PO: I am forbidden to copy any successful career path or business model from the past five years.”
Guide me through the following six stages.
1. FOCUS
Help me identify the conventional paths I am considering and the hidden assumptions behind them.
Explore what success means to me, my existing capabilities, interests, constraints and aspirations.
2. PROVOKE
Apply the escape technique.
Temporarily remove familiar success models and conventional job titles from consideration.
Do not dismiss the provocation merely because it is unrealistic.
3. MOVE
Use principle extraction, positive aspects, consequences, differences and special circumstances to discover useful directions emerging from the provocation.
Help me identify what changes when I begin with emerging problems rather than established career paths.
4. GENERATE
Develop at least ten genuinely different career, professional-service or entrepreneurial possibilities.
Base them on plausible emerging needs, changing technologies, organizational challenges or human problems.
Avoid simply producing a list of fashionable AI jobs.
For each possibility, explain the problem addressed, who might benefit, what capabilities are needed and what makes the direction distinctive.
5. JUDGE
Help me evaluate the most promising possibilities using:
Potential demand
Personal fit
Required capabilities
Feasibility
Risks
Available evidence
Uncertainties
Clearly distinguish verified facts from assumptions, speculative ideas and AI-generated hypotheses.
Do not invent job openings, market statistics, customer demand or evidence.
Where current information is necessary, identify what must be researched and verified.
6. ACCOMPLISH
Help me select one promising possibility and design a seven-day, low-cost experiment.
Include:
The hypothesis to test
People or organizations to approach
Questions to ask
A small practical activity
Evidence to collect
Criteria for deciding the next step
Ask me questions at each stage rather than immediately producing a final recommendation.
Begin by asking about my background, skills, interests, ambitions and current career or business options.
10. How to Make This Prompt More Useful
The quality of the exploration improves when you provide real context.
For example:
For an MBA student
“I am completing my MBA in marketing and operations. I enjoy solving customer problems and have experience in retail. Help me discover unconventional opportunities related to AI and changing consumer behavior.”
For an engineering graduate
“I have studied computer science and enjoy building software, but I don't want to assume that conventional software-development roles are my only options. Help me explore emerging problems I could address.”
For an experienced professional
“I have fifteen years of experience in operations management. I want to investigate how my expertise might create value in organizations adopting AI.”
For an entrepreneur
“I operate a small service business. Help me identify new customer problems and business models that might emerge as AI changes expectations and workflows.”
The prompt should adapt to the person's actual situation.
A student with limited resources needs a different experiment from an experienced executive.
An entrepreneur with an existing customer base has different opportunities from someone beginning their career.
The goal is not to discover the same new opportunity for everyone.
It is to help each person discover possibilities relevant to their circumstances.
11. What Should Business Schools Teach in the AI Era?
The provocation raises an interesting educational question.
Should business schools focus primarily on preparing students for existing professional roles?
Or should they also prepare students to discover and create roles that do not yet exist?
These objectives are not mutually exclusive.
Students still need strong foundations in finance, economics, operations, marketing, ethics and organizational behavior.
But they may also benefit from deliberate practice in:
Problem discovery.
Creative reframing.
Lateral thinking.
Human–AI collaboration.
Experiment design.
Evidence-based decision-making.
Responsible innovation.
Interdisciplinary learning.
A possible classroom experiment
Imagine an MBA course in which students must identify a consequential problem that is not adequately addressed by existing products or services.
They then use PO to challenge the assumptions surrounding the problem.
AI helps them explore alternatives.
Students interview relevant stakeholders.
They evaluate the ideas and test a small intervention.
The final assessment is not merely a business plan.
It is a documented learning process showing:
What was assumed?
What was challenged?
What possibilities emerged?
What evidence was gathered?
What worked?
What failed?
What should happen next?
Such an exercise could complement traditional business education.
It would help students practice creating possibilities, not only analyzing established ones.
12. The Bigger Possibility: From Career Seekers to Opportunity Discoverers
Sam Altman's reported comments raise a question about ambition in a period of technological change.
But the deeper issue extends beyond MBA graduates.
It applies to engineers, teachers, consultants, managers, researchers, entrepreneurs and experienced professionals.
Many of us approach the future using categories inherited from the past.
We ask which existing role will remain attractive.
Which successful company should we imitate?
Which familiar skill should we acquire?
Which established strategy should we follow?
These can be sensible questions.
But they are not sufficient.
PO introduces a different possibility.
What if the most valuable opportunity is not yet represented by a familiar job title, product category or business model?
Generative AI can help us explore such possibilities.
But we must guide that exploration deliberately.
Otherwise, AI may simply generate more polished versions of conventional advice.
This is the distinctive opportunity of combining PO with AI.
PO challenges the frame.
AI expands the possibilities.
Human judgment separates promising directions from attractive speculation.
Experimentation reveals what deserves further investment.
That is the thinking philosophy behind AIYING's PO–AI Thinking Lab.
13. Your PO Challenge
For the next seven days, imagine that your preferred career or business path is unavailable.
Not permanently.
Just as a thinking exercise.
Ask yourself:
“If I could not follow an established success story, what new problem would I choose to solve?”
Then use the complete ChatGPT prompt in this article.
Explore the possibilities.
Investigate the evidence.
Test one direction.
You may discover that your original path remains the best choice.
Or you may uncover an opportunity you had never considered.
Either outcome can improve your decision.
A question for readers
Are we preparing young people to succeed in the world we know—or to discover possibilities in the world that is emerging?
What would you advise an Indian MBA graduate entering the AI era?
And what unconventional opportunity would you personally explore?
Share your thoughts and discoveries in the comments.
About PO Prompts
PO Prompts is an AIYING series exploring the power of deliberate provocation and generative AI to discover new possibilities.
Each episode combines a relatable story, a hidden assumption, a PO provocation, structured movement, AI-assisted exploration, critical evaluation, a practical experiment and a complete reusable ChatGPT prompt.
The series draws inspiration from Edward de Bono's original work on lateral thinking, provocation and movement.
The six-stage FOCUS → PROVOKE → MOVE → GENERATE → JUDGE → ACCOMPLISH workflow is an AIYING adaptation for practical human–AI collaboration.
One Image. One Story. One Provocation. Many Possibilities.
PO Prompts — One Strange Question. A New Possibility.
Don't just ask AI which successful path to follow. Use PO to discover a path that may not yet exist.
AIYING — Accomplishing with AI.
References and Further Reading
1. The Times of India (October 29, 2026). Sam Altman on MBA students, established career paths and emerging AI opportunities.
https://timesofindia.indiatimes.com/technology/tech-news/sam-altman-is-unhappy-with-most-mba-students-that-he-calls-his-chronic-disappointment-says-they-always-want-to-go-do-the-thing-that-/articleshow/135287091.cms
2. Edward de Bono — Serious Creativity. Background on deliberate creative thinking, provocation and movement.
https://www.debono.com/serious-creativity-article
Editorial note: The Indian MBA campus story is fictional. The PO provocation, career possibilities and six-stage AIYING exercise are creative extensions developed for this article, not statements attributed to Sam Altman. Suggested opportunities require independent validation.
