Introduction by Ray Dalio

Throughout history, investors have searched for ways to make better decisions with imperfect information. We’ve studied cycles, created models, built teams of analysts, and tried to separate signal from noise. Today, a new force has entered the game: artificial intelligence.

AI doesn’t feel fear, it doesn’t get greedy, it doesn’t forget its rules. It processes data faster than any of us could imagine. Yet, it cannot replace the essence of investing — judgment, philosophy, and character. That’s why this conversation is important. We’re here to explore not whether AI will change investing — it already has — but how we, as human beings, choose to use it.

The future edge will not come from having AI, because soon everyone will. The edge will come from how wisely we integrate it into our principles, how well we manage our emotions, and how clearly we define our goals. With that, let’s begin.

(Note: This is an imaginary conversation, a creative exploration of an idea, and not a real speech or event)

Topic 1: AI as the Investor’s Analyst: Where Machines End and Humans Begin

Moderator: John Shūgyō

John Shūgyō:
For centuries, investors have sought better data, faster analysis, and clearer insights. Today, AI offers to be our analyst, assistant, and sometimes even therapist. But let’s start here: where do you believe AI truly excels as an “analyst,” and where must humans still take the lead?

Cathie Wood:
AI is incredible at synthesizing massive streams of information. At ARK, we already use machine learning to scan patent filings, research papers, and alternative datasets. But AI cannot yet predict the disruptive leaps—the Teslas of tomorrow. That requires vision and conviction. Humans must still lead in recognizing paradigm shifts.

Andrew Ng:
I agree. AI is powerful at pattern recognition and information retrieval. It can tell you what the data says, but not what matters most. The challenge is context. Investors must supply the purpose, the framing, the “why.” Without human direction, AI’s brilliance is like a flashlight in the wrong direction.

Ray Dalio:
I’ve always said investing is about making decisions under uncertainty, guided by principles. AI is great at enforcing consistency—it won’t forget your rules or betray them under stress. But the deeper judgment—how geopolitical shifts or cultural dynamics will play out—remains human. AI doesn’t yet understand human nature as history teaches us.

Cliff Asness:
From a quant perspective, I love AI’s ability to crunch absurd amounts of data. But beware: it’s not magic. If your inputs are garbage or your model reflects yesterday’s regime, AI will still mislead you. Humans must decide what questions to ask and how to guard against overfitting.

Aswath Damodaran:
Finance is ultimately about valuation. AI can automate number-crunching, but it cannot replace the human act of assigning meaning. Is Amazon overpriced? Is Tesla justified? These hinge not just on spreadsheets but on stories about the future. That’s where humans must remain storytellers and skeptics.

John Shūgyō:
Thank you. Now let’s push deeper: If AI helps investors avoid emotional bias, does that risk turning markets into “cold machines”? What role do you see for human intuition and gut feeling in this new landscape?

Ray Dalio:
Markets are already systems of competing algorithms and human impulses. Removing emotion can reduce crashes born of panic, but it can also strip away creativity. Intuition is pattern recognition born of experience. AI doesn’t replace it—it complements it.

Cliff Asness:
I’ll be blunt: gut instinct is often just noise. AI can enforce discipline, which is good. But sometimes, gut matters when you’re spotting what the data can’t yet show. The trick is humility—knowing when to override the model and when not to.

Cathie Wood:
Intuition is critical for seeing disruption before the numbers catch up. Data lagged on Tesla for years while our conviction grew. AI would have told us “too risky.” Human imagination turned that into a 10x opportunity. We must not outsource courage to code.

Aswath Damodaran:
I think intuition without grounding is dangerous. But intuition built on years of valuing companies—that’s a kind of subconscious analysis AI can’t replicate. AI can aid, but it can’t substitute the years in the trenches that shape real investor instinct.

Andrew Ng:
Humans excel at defining goals. AI excels at executing within those goals. Intuition plays a role in setting the right objectives: what industries to study, what risks to accept. Without that, AI is efficient but directionless.

John Shūgyō:
Excellent points. Let me close with this: When AI becomes available to every investor, what separates those who thrive from those who simply follow? How do you keep the edge?

Aswath Damodaran:
Your edge will come not from having AI, but from asking better questions of it. Most investors will ask, “What stock should I buy?” The insightful ones will ask, “What story am I missing in this valuation?”

Cathie Wood:
You win by having unique hypotheses. If everyone uses the same AI, they’ll converge on the same conclusions. We’ll thrive by embedding our philosophy—our belief in innovation, disruption—into AI prompts, making it an extension of conviction, not a substitute.

Cliff Asness:
Edge will come from skepticism. AI can hallucinate or overfit just like humans. Those who test, verify, and challenge AI outputs will outperform those who blindly accept them.

Ray Dalio:
I’d add that principle-driven investors will still lead. AI is a tool, not a compass. Investors with timeless principles, who can encode them and enforce them through AI, will outlast trends.

Andrew Ng:
And technically, the edge may shift to how well you engineer prompts, integrate real-time data, and combine multiple AIs. It’s less about having access, more about skillful orchestration.

John Shūgyō (closing remarks):
So, AI as analyst brings us closer to clarity and discipline—but never absolves us of responsibility. The message is clear: AI levels the playing field, but humans who ask sharper questions, embed philosophy, and test relentlessly will still rise above the crowd.

Topic 2: The Emotional Side of Money: Can AI Really Calm Investor Bias?

Moderator: John Shūgyō

John Shūgyō:
Investing is as much about managing our own emotions as it is about managing money. Fear, greed, panic, and overconfidence have ruined more fortunes than poor spreadsheets ever did. AI claims to help regulate this by offering data-driven calm. But let me ask each of you: can AI truly become a stabilizer for investor psychology, or is this just an illusion?

Daniel Kahneman:
Bias is stubborn. AI can help by surfacing data that corrects our misperceptions, but it cannot change the human condition. Loss aversion, overconfidence—these are hardwired. If anything, AI can make investors more confident in their biases if they misuse it. The stabilizing force will come only if investors treat AI as a mirror, not a crutch.

Esther Perel:
I see it less as illusion, more as possibility. Humans often spiral alone in anxiety. When an AI voice calmly says, “The data doesn’t support panic,” that can soothe. But emotional care is not just numbers—it’s relationship. The danger is outsourcing comfort instead of cultivating resilience. AI may calm us, but healing fear is still a human task.

Howard Marks:
Markets run on psychology. Every cycle I’ve studied shows that. AI can temper excess optimism or fear by giving us historical perspective at speed. But investors don’t fail for lack of data—they fail because they ignore the data when emotions roar. AI can lower the temperature, but discipline remains our job.

Morgan Housel:
I’ll add: AI can be a therapist for the spreadsheet. It can track behavior, remind you of past mistakes, and highlight patterns. But like therapy, it only works if you listen. Most people won’t. Fear feels real, logic feels abstract. AI can show us truth, but can it make us believe it? That’s the human gap.

Dan Ariely:
Yes, bias is irrational, but we can design systems to guide us. Imagine an AI that blocks you from panic-selling below your stop-loss, or nudges you with “remember your plan.” That’s not illusion—it’s intervention. We can embed guardrails. The question is not whether AI can calm us, but whether investors allow it to discipline them.

John Shūgyō:
Thank you. Let’s push further: fear and greed have always driven bubbles and crashes. If AI starts guiding millions of investors away from emotional extremes, does that make markets safer—or more fragile in a new way?

Howard Marks:
Markets thrive on diversity of opinion. If everyone uses the same AI guardrails, we risk uniform behavior, which can actually amplify fragility. Imagine millions of stop-loss triggers firing at once. AI could make the crowd more synchronized, and synchronization is dangerous.

Morgan Housel:
I agree. If AI reduces extremes, we might avoid some bubbles, but we might also lose the randomness that creates opportunity. Markets are like ecosystems—they need variety, including irrationality. Too much rational AI-driven behavior could ironically make the system brittle.

Dan Ariely:
True, but consider the alternative: unchecked panic leads to crashes anyway. The advantage of AI is that we can build different personalities into it. One AI can be cautious, another contrarian, another momentum-driven. Diversity can be engineered if we’re thoughtful.

Esther Perel:
What I hear is a deeper question: are we trying to make markets healthier, or humans healthier? AI may create stability in numbers but still leave individuals emotionally hollow. If every decision is outsourced to calm logic, do we grow as emotional beings? Stability in markets could come at the cost of resilience in humans.

Daniel Kahneman:
Markets will always be fragile, because humans are fragile. AI may dampen the peaks and troughs, but it cannot eliminate cycles. It might shift the shape of risk, but risk itself remains. The important thing is not to imagine a world without bubbles, but to prepare for different kinds of bubbles.

John Shūgyō:
Powerful insights. Now let me ask the final, most personal question for this topic: If you could design your own AI coach to guide you during moments of maximum fear or greed, what would you want it to say—or not say?

Esther Perel:
I’d want it to say, “Pause. Breathe. What story are you telling yourself about this loss?” Fear is a narrative we construct. A good AI would not just give data—it would invite reflection. I’d want compassion, not just calculation.

Dan Ariely:
Mine would be strict. “Stop. You set this plan for a reason. Don’t betray yourself.” Like a behavioral contract, enforcing the rules I created when I was rational. Sometimes we need a tough coach, not a gentle friend.

Morgan Housel:
I’d want it to remind me: “This has happened before. Remember the dot-com bust? Remember 2008? You survived.” Memory is the most powerful antidote to panic. AI has perfect memory—we don’t. That’s its greatest gift.

Howard Marks:
I’d want it to remind me of my own memos. “Howard, you’ve written this before.” Because in truth, we forget our own wisdom under stress. A good AI should echo your best self back to you.

Daniel Kahneman:
I would want it to say nothing. Bias is not corrected by words in the heat of the moment—it’s corrected by structures set in advance. If my AI simply executed my pre-committed rules, silently, that would be its greatest service.

John Shūgyō (closing remarks):
What a fascinating discussion. We’ve heard that AI can be therapist, disciplinarian, memory-keeper, or silent executor. But all of you agree on one thing: AI cannot erase emotion—it can only hold a mirror, build guardrails, or echo our best selves. The human challenge remains: to decide whether we will listen.

Topic 3: Choosing the Right AI: Claude, ChatGPT, Perplexity, Gemini

Moderator: John Shūgyō

John Shūgyō:
Investors today face a new kind of decision: not just which stock to buy, but which AI to trust. Each system—ChatGPT, Claude, Perplexity, Gemini—offers different strengths. To begin: what is the unique advantage of your AI in helping investors, and where should they be cautious?

Sam Altman:
ChatGPT is a generalist. Its strength is flexibility—you can mold it into a market analyst, a historian of cycles, or even a therapist for decision-making. But caution: it doesn’t fetch real-time market data natively. You must supply reliable inputs. Garbage in, garbage out remains true.

Dario Amodei:
Claude excels at clarity and context. Investors often drown in documents—earnings calls, regulatory filings. Claude can digest and return not just summaries but nuanced reasoning. The limitation? Like all AIs, it can hallucinate. Investors must still verify.

Aravind Srinivas:
Perplexity is designed for real-time search. Investors don’t just need yesterday’s filings—they need the latest analyst notes, regulatory changes, breaking news. That’s where we shine. But our caution: speed doesn’t always mean accuracy. Users must weigh fresh information against verified fundamentals.

Sundar Pichai:
Gemini’s advantage lies in integration. Google connects across data sources—search, cloud, even translation. For investors, this means broader context: macroeconomic trends, sector-level shifts, language-specific insights. But integration also brings risk: complexity. Investors must learn to ask focused questions to avoid overload.

Demis Hassabis:
DeepMind’s philosophy is science-first. We’re not just building an assistant—we’re pushing toward reasoning AIs. For investors, this means better scenario modeling in the future. But we must be cautious not to overpromise. AI is powerful, but predicting markets will always remain uncertain.

John Shūgyō:
Thank you. Now let’s dig deeper: Investors often want “the answer”—buy, sell, hold. But should AIs give answers, or should they remain tools for thinking? How do you see that balance?

Aravind Srinivas:
Perplexity is a compass, not a captain. We deliver the latest maps—investors still choose the route. If we give “buy this now” answers, we risk misleading. Better to empower the human with evidence.

Sam Altman:
I agree. ChatGPT is best as a thinking partner. We’ve seen users treat it as an oracle, but that’s dangerous. The future lies in co-intelligence: humans and AI trading insights back and forth.

Dario Amodei:
Claude is intentionally cautious about certainty. We frame outputs in probabilities, with reasoning. That forces the human to remain responsible. If AI becomes too directive, it encourages abdication of judgment, which is unhealthy for markets.

Sundar Pichai:
Gemini is built to offer multiple perspectives. Rather than “buy or sell,” it can lay out scenarios: what happens if interest rates rise, what if supply chains break. The responsibility for the decision must rest with the investor.

Demis Hassabis:
We’ve studied reinforcement learning for years. One truth holds: systems must avoid over-optimization for single answers, because that breeds fragility. Investors thrive on resilience, not certainty. AI should augment resilience, not replace uncertainty with false clarity.

John Shūgyō:
Excellent. Let me close with this: fast-forward 10 years—do you envision a future where investors interact with AI the way they once did with human brokers and analysts? What will that relationship look like?

Sam Altman:
Yes, but more intimate. Millions will have a personal AI that knows their risk tolerance, history, even emotions. It will feel like having a private Morgan Stanley desk, but personalized.

Sundar Pichai:
The key is scale. AIs will democratize insights once reserved for billion-dollar funds. But they will also reshape markets: collective use will amplify certain signals. We’ll need guardrails to preserve diversity of thought.

Aravind Srinivas:
I see investors moving from “data hunting” to “hypothesis testing.” Your AI will fetch all the data instantly. Your job will be to generate original hypotheses. That’s how humans stay relevant.

Dario Amodei:
In 10 years, AI may feel less like a tool and more like a colleague. You’ll argue with it, push back, refine. The most successful investors will be those who treat AI as an equal partner, not a master.

Demis Hassabis:
I’ll be cautious: if we do this wrong, investors will treat AI as prophets. That’s dangerous. But if we do it right, we’ll build systems that teach humans while guiding them. A future where investors emerge smarter, not lazier.

John Shūgyō (closing remarks):
So, the verdict is clear: AI is not a replacement for the investor, but a thinking partner, a compass, a colleague. Each tool—ChatGPT, Claude, Perplexity, Gemini—offers a different strength, but all leaders here agree on one thing: investors must remain accountable. The edge will belong not to those who follow AI blindly, but to those who converse with it wisely.

Topic 4: The Future of Edge: When Everyone Uses AI, How Do You Stay Ahead?

Moderator: John Shūgyō

John Shūgyō:
For centuries, investing has been about finding an edge—information, insight, or instinct others don’t have. But with AI becoming accessible to all, the playing field looks flatter than ever. Let me start here: in a world where everyone uses AI, where will the next true edge come from?

Warren Buffett:
The edge will always be temperament, not tools. Everyone will have the same machines, just like everyone has the same financial statements today. What matters is patience, discipline, and the ability to sit still while others chase excitement. Technology doesn’t change human nature—that’s where the real edge lies.

Elon Musk:
I’d argue the edge will be in velocity. Whoever can integrate AI into real-world execution fastest wins. In cars, rockets, energy, I’ve seen that the boldest iteration speed creates advantage. Investors who use AI not just to analyze but to act, to move, will lead.

Chamath Palihapitiya:
I think it’s about contrarianism. AI will push investors toward consensus—“safe” bets backed by data. The edge will be having the courage to go against the machine-driven herd. If AI says 90% probability of X, sometimes the best money is on the 10%.

Cliff Asness:
As a quant, I’ve seen this before. Once everyone had Bloomberg terminals, data stopped being an edge. The edge came from models, then from implementation. With AI, the same rule applies: edge will be in how you combine signals, how you stress-test, how you manage risk when the model fails.

Marc Andreessen:
The edge will come from imagination. AI gives you infinite analysis, but it can’t tell you what doesn’t exist yet. The biggest fortunes will be made by those who bet on the unmodeled—the next industry, the next platform. AI is backward-looking. Vision is forward-looking.

John Shūgyō:
Excellent. Now let’s push this further: if AI creates a world of uniform analysis, do we risk markets moving as one giant herd? What dangers, or opportunities, does that create?

Cliff Asness:
Uniformity is a nightmare. If everyone is reading from the same AI playbook, correlations spike. That means bigger crashes when things go wrong. Herd behavior amplified by machines can be catastrophic. But it also creates opportunities—for those willing to step aside until the herd stampedes past.

Chamath Palihapitiya:
I think the danger is real, but overstated. Most people don’t listen to AI perfectly, just like they don’t listen to data perfectly now. Human greed and fear will still create divergence. The opportunity is in knowing when AI consensus is right—and when it’s dangerously wrong.

Elon Musk:
Markets are already algorithmic herds. AI just makes the algorithms smarter. But here’s the twist: AI can also simulate herd behavior in advance. If you know what 10 million AI-assisted traders will do, you can position ahead of them. That’s the meta-edge—front-running the machine herd.

Marc Andreessen:
Yes, and here’s the paradox: homogeneity breeds fragility, but fragility breeds opportunity. Crashes caused by uniform AI strategies will wipe out the average investor, but create generational buying chances for the few who remain liquid and contrarian.

Warren Buffett:
I’ll remind everyone: it doesn’t matter what the crowd does if you know the value of what you own. Herds come and go. Crashes come and go. If your foundation is solid, the herd is just noise. The edge is character, not cleverness.

John Shūgyō:
Let’s finish strong. If you were advising a young investor entering this AI-driven market, what one piece of advice would you give them to cultivate a lasting edge?

Marc Andreessen:
Don’t just consume AI outputs—use AI to build new mental models. The investor who sees differently, not just faster, wins.

Cliff Asness:
Test everything. AI is seductive, but fragile. Your edge will be in skepticism—verify, falsify, and survive.

Chamath Palihapitiya:
Have the guts to disagree. If AI tells you to follow the crowd, ask yourself: “What if it’s wrong?” Contrarianism is lonely, but profitable.

Elon Musk:
Move fast. Don’t wait for perfect data. AI will paralyze some people with endless scenarios. Edge comes from execution before certainty.

Warren Buffett:
Keep it simple. The edge has always been knowing what you understand, and ignoring what you don’t. AI won’t change that. The best investment edge is clarity of mind.

John Shūgyō (closing remarks):
So, the edge in an AI-saturated world isn’t about who has access—because everyone will. It’s about who has courage, patience, skepticism, imagination, and execution speed. AI may level the field, but it will also amplify differences in character. And as our panel reminds us: the true edge will always be human.

Topic 5: Building AI-Enhanced Investment Philosophies

Moderator: John Shūgyō

John Shūgyō:
Every great investor has a philosophy—Buffett’s value, Dalio’s principles, Marks’s risk awareness, Wood’s innovation conviction. AI adds power, but also risk of drifting into machine-driven uniformity. So let me start here: how can investors embed their personal philosophy into AI, instead of being consumed by it?

Ray Dalio:
Philosophy is what guides decisions when data is unclear. You can encode rules into AI—if inflation rises, adjust bonds this way; if valuations exceed X, be cautious. But philosophy is more than code. It’s lived experience. Investors should make AI reflect their principles, not replace them.

Morgan Housel:
I’d say philosophy comes down to stories. Why you invest, what you believe about the future, what risks you’re willing to take. If you train AI with your history—your choices, your mistakes, your wins—it becomes a mirror of your philosophy. But it starts with knowing your story yourself.

Howard Marks:
Risk is central to philosophy. Too many treat AI as if it eliminates risk. It doesn’t. You must feed AI not just “find me winners,” but “show me what could go wrong.” If your philosophy includes humility about the future, you’ll design prompts that keep risk in focus.

Andrew Ng:
From a technical view, philosophy becomes constraints. AI can test thousands of strategies, but your philosophy decides which constraints matter: sustainability, ethical investing, volatility tolerance. Prompts are philosophy in code. The better you know yourself, the better your AI serves you.

Cathie Wood:
For me, philosophy is conviction in innovation. AI can accelerate research, but conviction must be human. We can teach AI to surface disruptive ideas—but only humans decide to bet on them before they’re obvious. Philosophy is the courage to be early, not just the wisdom to be right.

John Shūgyō:
That’s powerful. Let’s move deeper: if philosophy is encoded into AI, do we risk creating a new rigidity—where investors stop adapting because “their AI” enforces old rules? How do we keep flexibility?

Howard Marks:
Rigidity is always a danger. The answer is periodic review. Just as we rebalance portfolios, we must rebalance philosophies. AI can even remind us: “Your rule hasn’t fit reality for five years—should we revise it?” Flexibility is discipline plus reflection.

Ray Dalio:
I agree. Principles are timeless, but applications are situational. My principle: don’t bet against productivity growth. The application—where, when, how—changes with time. If AI enforces principles blindly, it ossifies. If it helps test them in new contexts, it strengthens them.

Andrew Ng:
Technically, this means designing AI to question, not just follow. “Does your rule still apply?” “Have conditions shifted?” Good AI can become your Socratic partner, pushing you to rethink. Rigidity is avoided if AI is instructed to challenge, not just obey.

Cathie Wood:
Yes, flexibility means listening for signals outside your comfort zone. My philosophy embraces disruption, but AI can help show me when disruption is not yet investable. That tension keeps me adaptive. Philosophy is the anchor, but AI is the wind that pushes you to adjust.

Morgan Housel:
I’d add: philosophies survive when they’re principles, not predictions. “Humans are greedy and fearful” is timeless. “Tech stocks always win” is not. If your AI is trained on predictions, rigidity is fatal. If it’s trained on principles, flexibility is built in.

John Shūgyō:
Excellent. One last question: What does an AI-enhanced philosophy look like 20 years from now? Will we think of AI as just a tool, or as part of our identity as investors?

Cathie Wood:
I believe it becomes identity. Every investor will have a personal AI, tuned to their values. It won’t just give answers; it will express who they are. Philosophy won’t disappear—it will be amplified through technology.

Morgan Housel:
I think it will look like a diary. Twenty years of decisions, mistakes, and reflections stored in AI, guiding the next generation. It becomes not just your philosophy, but your legacy.

Andrew Ng:
From the technical side, AI will blend with us seamlessly. You won’t think “I’m using AI.” It will be like electricity—always there. Philosophies won’t be “AI-enhanced” anymore; they’ll just be the way we think.

Howard Marks:
But I caution: identity must remain human. AI can remind, test, enforce, but philosophy comes from values, not data. Twenty years from now, the best investors will still be those with character—who use AI without becoming it.

Ray Dalio:
I see it as a merger. Humans with principles, machines with analysis, combined into a single decision-making system. But the human must remain the captain. Twenty years from now, those who confuse the machine for the captain will pay the price.

John Shūgyō (closing remarks):
So, the future of investment philosophy with AI is not about surrendering judgment—it’s about codifying wisdom, testing it, and letting it evolve. The message is clear: philosophy gives direction, AI provides scale, but character makes the final call.

Final Thoughts by Warren Buffett

When you’ve lived through as many market cycles as I have, you learn that tools change but people don’t. AI is powerful, no doubt about it. It can give you speed, analysis, maybe even comfort. But it cannot give you discipline. It cannot give you patience. It cannot give you the temperament to sit still when the world is screaming.

The most important thing about investing has never been information. It has been judgment — the ability to know what matters, to ignore what doesn’t, and to stick with what you understand. AI can help, but it cannot decide for you.

In the end, the responsibility is yours. The gains and losses are yours. And the satisfaction of saying, ‘I made the right decision for the right reason’ — that will always belong to the human being, not the machine.

So use AI. Learn from it. Let it sharpen you. But never forget: your greatest edge is not in algorithms. It is in character.

Short Bios:

John Shūgyō (J. Jung) is President of TBL Investment Academy, a management consultant, and author of The Textbook of Generative AI Investing. He specializes in connecting AI with practical investment strategies for individuals.

Ray Dalio is the founder of Bridgewater Associates, one of the world’s largest hedge funds, and the author of Principles. Known for his systematic, principle-driven approach, he is a leading thinker in macroeconomics and investing.

Warren Buffett is Chairman and CEO of Berkshire Hathaway and among the most successful investors of all time. He is celebrated for his disciplined value-investing philosophy and long-term patience.

Cathie Wood is the founder and CEO of ARK Invest, famous for her bold, innovation-focused strategies that emphasize disruptive technologies and high-growth companies.

Aswath Damodaran is a professor of finance at NYU Stern School of Business, widely regarded as the “Dean of Valuation” for his expertise in company valuation and financial modeling.

Cliff Asness is co-founder of AQR Capital Management, a global leader in quantitative investing. He is recognized for pioneering systematic and evidence-based strategies in finance.

Howard Marks is co-founder of Oaktree Capital Management, known worldwide for his influential memos and insights into market cycles, risk, and investor psychology.

Morgan Housel is a partner at Collaborative Fund and the author of The Psychology of Money, renowned for his ability to explain finance through human stories and timeless lessons.

Daniel Kahneman was a Nobel Prize–winning psychologist and economist, co-author of Thinking, Fast and Slow. His groundbreaking work on cognitive biases reshaped behavioral finance.

Dan Ariely is a behavioral economist at Duke University and the author of Predictably Irrational, focusing on how emotions and irrational behavior influence financial decisions.

Esther Perel is a psychotherapist and bestselling author, globally recognized for her insights on human behavior, resilience, and emotional patterns—bringing a unique perspective to financial decision-making.

Sam Altman is the CEO of OpenAI, builder of ChatGPT, and a major investor in breakthrough technologies. He is a leading voice in shaping AI’s role in society.

Dario Amodei is CEO and co-founder of Anthropic, developer of Claude, and a key figure in building safe, interpretable AI systems.

Aravind Srinivas is CEO of Perplexity AI, leading innovation in real-time, conversational AI search and information retrieval.

Sundar Pichai is CEO of Alphabet and Google, responsible for global AI initiatives including Gemini, and a central figure in integrating AI into everyday tools.

Demis Hassabis is the CEO of DeepMind, known for developing AlphaGo and pushing the frontiers of advanced reasoning in AI research.

Elon Musk is the CEO of Tesla and SpaceX, founder of xAI, and one of the world’s most influential entrepreneurs driving the intersection of technology, markets, and innovation.

Marc Andreessen is co-founder of Andreessen Horowitz, one of Silicon Valley’s most influential venture capital firms, and a visionary in spotting transformative technologies.

Chamath Palihapitiya is CEO of Social Capital, a venture capitalist known for contrarian thinking and investing in disruptive innovations that challenge conventional wisdom.

Andrew Ng is co-founder of Coursera, founder of DeepLearning.AI, and one of the most influential AI educators and pioneers, recognized for making AI practical and widely accessible.