“If it doesn't work, you deserve to be fired.”
Scott Kirby, CEO of United Airlines, doesn't mince words when it comes to making big investment decisions.
He says Wall Street often dislikes United's long-term investments, particularly in aircraft. Investors want lower costs, but also mean sacrificing the company's future.
His message to other CEOs is simple: Stop complaining about short-term pressure. Make the investments you believe are right. And accept that your job may be on the line if they fail.
I agree that leadership requires that kind of personal responsibility.
But a bad outcome doesn't automatically mean it was a bad decision.
Investments involve uncertainty. Even well-founded decisions can fail.
That doesn't mean leaders should escape accountability. It means we need to look at more than the final result.
Were the assumptions sound? Were the risks understood? Did the leader respond when circumstances changed?
Leaders should be accountable for results. But judging their decisions only by whether they succeeded encourages exactly the kind of risk avoidance that makes long-term investment so difficult.
Source: Scott Kirby, CEO of United Airlines, on Cheeky Pint
Scott Kirby, CEO of United Airlines, doesn't mince words when it comes to making big investment decisions.
He says Wall Street often dislikes United's long-term investments, particularly in aircraft. Investors want lower costs, but also mean sacrificing the company's future.
His message to other CEOs is simple: Stop complaining about short-term pressure. Make the investments you believe are right. And accept that your job may be on the line if they fail.
I agree that leadership requires that kind of personal responsibility.
But a bad outcome doesn't automatically mean it was a bad decision.
Investments involve uncertainty. Even well-founded decisions can fail.
That doesn't mean leaders should escape accountability. It means we need to look at more than the final result.
Were the assumptions sound? Were the risks understood? Did the leader respond when circumstances changed?
Leaders should be accountable for results. But judging their decisions only by whether they succeeded encourages exactly the kind of risk avoidance that makes long-term investment so difficult.
Source: Scott Kirby, CEO of United Airlines, on Cheeky Pint
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35
Would you bench one of your best players when you're fighting for the title?
Arsène Wenger did exactly that at AS Monaco.
Jürgen Klinsmann recalls how Wenger repeatedly left the young Youri Djorkaeff out of the starting lineup, even as Monaco competed with Marseille for the n't understand it.
Djorkaeff was one of their best players. They needed him to win.
But Wenger saw something Klinsmann was overlooking.
Djorkaeff had the talent, but according to Wenger, he hadn't yet learned what it meant to be a professional.
So Wenger kept him on the bench.
And over the following months, Klinsmann watched Djorkaeff become increasingly professional.
I find this fascinating because Wenger wasn't choosing between winning and developing people.
He refused to let the pressure to win determine the standards he expected from his players.
That's a difficult position for any leader.
When results are under pressure, it's tempting to make exceptions for your strongest performers. But those exceptions also tell everyone else which standards really matter.
Wenger was willing to make an unpopular decision because he believed the player's development mattered beyond the next match.
Would you hold your best performer to the same standards as everyone else, even if it meant losing their contribution when you needed it most?
Source: Jürgen Klinsmann on Arsène Wenger, Stick to Football, The Overlap, Episode 131
Arsène Wenger did exactly that at AS Monaco.
Jürgen Klinsmann recalls how Wenger repeatedly left the young Youri Djorkaeff out of the starting lineup, even as Monaco competed with Marseille for the n't understand it.
Djorkaeff was one of their best players. They needed him to win.
But Wenger saw something Klinsmann was overlooking.
Djorkaeff had the talent, but according to Wenger, he hadn't yet learned what it meant to be a professional.
So Wenger kept him on the bench.
And over the following months, Klinsmann watched Djorkaeff become increasingly professional.
I find this fascinating because Wenger wasn't choosing between winning and developing people.
He refused to let the pressure to win determine the standards he expected from his players.
That's a difficult position for any leader.
When results are under pressure, it's tempting to make exceptions for your strongest performers. But those exceptions also tell everyone else which standards really matter.
Wenger was willing to make an unpopular decision because he believed the player's development mattered beyond the next match.
Would you hold your best performer to the same standards as everyone else, even if it meant losing their contribution when you needed it most?
Source: Jürgen Klinsmann on Arsène Wenger, Stick to Football, The Overlap, Episode 131
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52
How do you know whether a limit is real or just something nobody has challenged?
Eric Jorgenson, author of The Book of Elon, describes how Elon Musk approaches engineering at SpaceX.
In one example, Starship engineers were concerned about reducing the thickness of stainless directly to the people doing the welding. When they suggested that 4.5 millimeters might already be pushing it, he asked them to try 4.
According to Jorgenson, it worked.
What's interesting here isn't simply that Musk pushed for a more ambitious design.
He had enough technical understanding to challenge an assumption rather than just demand a better result.
And he understood the economics of scale.
If you're going to manufacture 10,000 engines, even a small inefficiency becomes something you reproduce 10,000 times.
That creates a strong incentive to discover the limits early, when experiments are still relatively cheap.
But there's a distinction leaders shouldn't miss.
Challenging an assumption requires understanding the work. Without that understanding, pushing people harder can easily become pressure without direction.
You don't need to be the best engineer in the room. But you should understand enough to know which questions are worth asking.
Source: Eric Jorgenson, author of The Book of Elon, in conversation with David Senra
Eric Jorgenson, author of The Book of Elon, describes how Elon Musk approaches engineering at SpaceX.
In one example, Starship engineers were concerned about reducing the thickness of stainless directly to the people doing the welding. When they suggested that 4.5 millimeters might already be pushing it, he asked them to try 4.
According to Jorgenson, it worked.
What's interesting here isn't simply that Musk pushed for a more ambitious design.
He had enough technical understanding to challenge an assumption rather than just demand a better result.
And he understood the economics of scale.
If you're going to manufacture 10,000 engines, even a small inefficiency becomes something you reproduce 10,000 times.
That creates a strong incentive to discover the limits early, when experiments are still relatively cheap.
But there's a distinction leaders shouldn't miss.
Challenging an assumption requires understanding the work. Without that understanding, pushing people harder can easily become pressure without direction.
You don't need to be the best engineer in the room. But you should understand enough to know which questions are worth asking.
Source: Eric Jorgenson, author of The Book of Elon, in conversation with David Senra
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142
The most experienced person in the room isn't always the one thinking hardest about the problem.
James Dyson makes a fascinating argument for hiring people who don't yet know all the reasons something won't work.
Experienced engineers have seen ideas fail. They recognize approaches to avoid.
But a young engineer might question an assumption that everyone else has accepted for years.
Dyson argues that not knowing the answer can actually be an advantage. Instead of reaching for a familiar solution, you're forced to figure things out.
I think this is something leaders need to pay attention to.
**Experience is valuable because it helps us recognize patterns. It becomes dangerous when we stop questioning whether those patterns still apply.**
That's why having different levels of experience in a team matters. Not because younger people are automatically more innovative, but because they may challenge assumptions that experienced people no longer notice.
And leaders have to make sure those questions are taken seriously, even when they come from the least experienced person in the room.
Source: James Dyson in conversation with David Senra
#Leadership #Innovation #ProblemSolving
James Dyson makes a fascinating argument for hiring people who don't yet know all the reasons something won't work.
Experienced engineers have seen ideas fail. They recognize approaches to avoid.
But a young engineer might question an assumption that everyone else has accepted for years.
Dyson argues that not knowing the answer can actually be an advantage. Instead of reaching for a familiar solution, you're forced to figure things out.
I think this is something leaders need to pay attention to.
**Experience is valuable because it helps us recognize patterns. It becomes dangerous when we stop questioning whether those patterns still apply.**
That's why having different levels of experience in a team matters. Not because younger people are automatically more innovative, but because they may challenge assumptions that experienced people no longer notice.
And leaders have to make sure those questions are taken seriously, even when they come from the least experienced person in the room.
Source: James Dyson in conversation with David Senra
#Leadership #Innovation #ProblemSolving
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24
In 2017, Volkswagen's CEO dismissed Tesla.
Matthias Müller pointed to Volkswagen's 11 million cars sold and billions in profit. @Tesla, he claimed, was struggling to sell 80,000 cars a year and losing money.
Nine years later, Tesla is an established global automaker. And ddle of a painful transformation.
On October 29, VW reported a €5.86 billion operating loss for the third quarter, heavily affected by exceptional charges, particularly at Porsche. The group is restructuring, reducing costs, and facing growing competition, particularly in China.
Volkswagen was enormously successful in 2017. Müller had every reason to be proud of what the company had achieved.
But he was judging Tesla by the standards of a business Volkswagen had spent decades perfecting.
Sales volume. Profitability. Scale.
Those numbers mattered. They just didn't tell the whole story.
Tesla wasn't trying to build a better version of Volkswagen. It was betting that the industry itself would change.
A leader's job isn't just to protect what made the company successful. It's to question whether those same assumptions will still hold tomorrow.
Müller wasn't wrong to point to Volkswagen's success. But success is a terrible reason to dismiss what you don't yet understand.
The real danger isn't having a competitor who is smaller and less profitable than you. It's assuming that makes them less relevant to your future.
Sources: Matthias Müller, then CEO of Volkswagen, Menschen in Europa forum, Passau, October 19, 2017 (original video: Passauer Neue Presse); Volkswagen Group, Q3 and 9M 2026 results, October 29, 2026.
Matthias Müller pointed to Volkswagen's 11 million cars sold and billions in profit. @Tesla, he claimed, was struggling to sell 80,000 cars a year and losing money.
Nine years later, Tesla is an established global automaker. And ddle of a painful transformation.
On October 29, VW reported a €5.86 billion operating loss for the third quarter, heavily affected by exceptional charges, particularly at Porsche. The group is restructuring, reducing costs, and facing growing competition, particularly in China.
Volkswagen was enormously successful in 2017. Müller had every reason to be proud of what the company had achieved.
But he was judging Tesla by the standards of a business Volkswagen had spent decades perfecting.
Sales volume. Profitability. Scale.
Those numbers mattered. They just didn't tell the whole story.
Tesla wasn't trying to build a better version of Volkswagen. It was betting that the industry itself would change.
A leader's job isn't just to protect what made the company successful. It's to question whether those same assumptions will still hold tomorrow.
Müller wasn't wrong to point to Volkswagen's success. But success is a terrible reason to dismiss what you don't yet understand.
The real danger isn't having a competitor who is smaller and less profitable than you. It's assuming that makes them less relevant to your future.
Sources: Matthias Müller, then CEO of Volkswagen, Menschen in Europa forum, Passau, October 19, 2017 (original video: Passauer Neue Presse); Volkswagen Group, Q3 and 9M 2026 results, October 29, 2026.
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35
Would you fire someone simply for being an average performer?
Nik Storonsky, co-founder and CEO of Revolut, says he would.
His argument? Too many executives put empathy ahead of winning. They hesitate to make tough personnel decisions because they care more about how people goal.
That's a provocative view of leadership.
My perspective: high standards and empathy belong together.
Empathy shouldn't become an excuse for avoiding difficult decisions. But demanding high performance doesn't give leaders permission to stop caring about people.
And there's an important distinction here.
Being an average performer isn't necessarily the same as failing to meet expectations. What matters is how those expectations are defined, communicated, and assessed.
I believe leaders have a responsibility to set ambitious standards and make difficult decisions. But they also have a responsibility to be fair about what they expect from people and how they treat them.
Should being average be enough to lose your job, even when you're meeting the expectations of your role?
Source: Nik Storonsky, Co-founder & CEO of Revolut, in conversation with David Senra
Nik Storonsky, co-founder and CEO of Revolut, says he would.
His argument? Too many executives put empathy ahead of winning. They hesitate to make tough personnel decisions because they care more about how people goal.
That's a provocative view of leadership.
My perspective: high standards and empathy belong together.
Empathy shouldn't become an excuse for avoiding difficult decisions. But demanding high performance doesn't give leaders permission to stop caring about people.
And there's an important distinction here.
Being an average performer isn't necessarily the same as failing to meet expectations. What matters is how those expectations are defined, communicated, and assessed.
I believe leaders have a responsibility to set ambitious standards and make difficult decisions. But they also have a responsibility to be fair about what they expect from people and how they treat them.
Should being average be enough to lose your job, even when you're meeting the expectations of your role?
Source: Nik Storonsky, Co-founder & CEO of Revolut, in conversation with David Senra
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45
A team can deliver everything you asked for. And you can still end up with a $750 million project that never reaches the market.
James Dyson spent five or six years developing an electric car before deciding to shut the project down.
They had working prototypes. But Dyson esla's scale and purchasing power. As a smaller manufacturer, his costs were roughly 30% higher.
When David Senra asked what he'd learned from the experience, Dyson's answer was remarkable.
“Absolutely nothing.”
Here's what I find interesting from a leadership perspective.
We hold teams accountable for execution. Deadlines, budgets, milestones, deliverables.
But delivering what was agreed doesn't automatically mean the investment still makes sense.
Leaders are accountable not just for how well work gets done, but whether it's still worth doing.
And that question becomes harder the longer a project runs, especially when the team is making visible progress.
If your team is delivering exactly what you asked for, but the business case no longer holds, who is responsible for saying stop?
Source: James Dyson in conversation with David Senra
James Dyson spent five or six years developing an electric car before deciding to shut the project down.
They had working prototypes. But Dyson esla's scale and purchasing power. As a smaller manufacturer, his costs were roughly 30% higher.
When David Senra asked what he'd learned from the experience, Dyson's answer was remarkable.
“Absolutely nothing.”
Here's what I find interesting from a leadership perspective.
We hold teams accountable for execution. Deadlines, budgets, milestones, deliverables.
But delivering what was agreed doesn't automatically mean the investment still makes sense.
Leaders are accountable not just for how well work gets done, but whether it's still worth doing.
And that question becomes harder the longer a project runs, especially when the team is making visible progress.
If your team is delivering exactly what you asked for, but the business case no longer holds, who is responsible for saying stop?
Source: James Dyson in conversation with David Senra
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24
“I can read my calendar. I don't need to summarize my day.”
That's Scott Kirby, CEO of one of the world's largest airlines, talking about AI.
He wasn't impressed by the usual productivity tricks. Summarizing documents, organizing calendars, all of that left him cold.
Then he tutoring.
One Saturday morning, between sets at his home gym, he built an application that compared betting odds across platforms and could place bets for him.
By the end of that morning, he had called his leadership team.
“We're all in on this.”
I love this example because Kirby didn't change his mind after hearing another presentation about AI.
He changed it after discovering what he could build himself.
This is exactly what I emphasize in my AI for Leaders workshops.
CEOs don't need to become software engineers. But they do need firsthand experience with AI. Otherwise, they're making decisions about a technology whose capabilities they only know through other people's descriptions.
Three hours didn't make Kirby an AI expert. They changed how he saw what was possible.
And that can change how you lead an entire organization.
Source: Scott Kirby, CEO of United Airlines, on Cheeky Pint
That's Scott Kirby, CEO of one of the world's largest airlines, talking about AI.
He wasn't impressed by the usual productivity tricks. Summarizing documents, organizing calendars, all of that left him cold.
Then he tutoring.
One Saturday morning, between sets at his home gym, he built an application that compared betting odds across platforms and could place bets for him.
By the end of that morning, he had called his leadership team.
“We're all in on this.”
I love this example because Kirby didn't change his mind after hearing another presentation about AI.
He changed it after discovering what he could build himself.
This is exactly what I emphasize in my AI for Leaders workshops.
CEOs don't need to become software engineers. But they do need firsthand experience with AI. Otherwise, they're making decisions about a technology whose capabilities they only know through other people's descriptions.
Three hours didn't make Kirby an AI expert. They changed how he saw what was possible.
And that can change how you lead an entire organization.
Source: Scott Kirby, CEO of United Airlines, on Cheeky Pint
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Mathieu Flamini spent nearly 20 years playing football at the highest level, including for Arsenal and AC Milan. Today, he's building a business that turns agricultural waste into alternatives to petroleum-based chemicals.
Quite a remarkable second career.
But what interests me om football into business.
Flamini talks about performing under pressure, making difficult decisions, and the commitment required to compete at the highest level.
His definition of leadership:
Great leadership combines high expectations with genuine human touch.
A great coach knows what their players are capable of. They don't lower the bar when things get difficult. But they also don't just demand better results and leave people to figure it out themselves.
They challenge, support, and develop their players.
Setting high standards is easy. Helping people reach them is the real work of leadership.
Source: Mathieu Flamini, Co-founder & CEO of GFBiochemicals, World Economic Forum
Quite a remarkable second career.
But what interests me om football into business.
Flamini talks about performing under pressure, making difficult decisions, and the commitment required to compete at the highest level.
His definition of leadership:
Great leadership combines high expectations with genuine human touch.
A great coach knows what their players are capable of. They don't lower the bar when things get difficult. But they also don't just demand better results and leave people to figure it out themselves.
They challenge, support, and develop their players.
Setting high standards is easy. Helping people reach them is the real work of leadership.
Source: Mathieu Flamini, Co-founder & CEO of GFBiochemicals, World Economic Forum
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77
Even Mark Zuckerberg doubted whether Facebook would succeed.
Ben Horowitz remembers spending time with Zuckerberg when he was just 22. Despite everything he was building, he wasn't sure it would work out.
That's something we tend to forget when looking at successful founders. es turned out, so we assume they somehow knew it too.
But they didn't.
Horowitz puts it beautifully: It's not what you feel. It's what you do.
Building a company means making decisions without knowing whether you're right. You can believe in what you're creating and still question yourself along the way.
The important part is not letting those doubts determine what you do next.
You don't have to resolve the uncertainty to keep moving forward.
Source: Ben Horowitz, Co-founder of Andreessen Horowitz, via a16z on X
Ben Horowitz remembers spending time with Zuckerberg when he was just 22. Despite everything he was building, he wasn't sure it would work out.
That's something we tend to forget when looking at successful founders. es turned out, so we assume they somehow knew it too.
But they didn't.
Horowitz puts it beautifully: It's not what you feel. It's what you do.
Building a company means making decisions without knowing whether you're right. You can believe in what you're creating and still question yourself along the way.
The important part is not letting those doubts determine what you do next.
You don't have to resolve the uncertainty to keep moving forward.
Source: Ben Horowitz, Co-founder of Andreessen Horowitz, via a16z on X
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23
Nik Storonsky thinks development teams of ten should become teams of two or three.
At Revolut, AI is already changing how work gets done. According to Storonsky, 90% of customer support is automated, and engineering productivity has increased by 30%.
But his next ambition goes yees with their own email, Slack accounts, access to internal systems, and the ability to join meetings and actually do the work.
Today, we divide work between departments, roles, and specialists. We build reporting lines, handovers, and coordination mechanisms around those divisions.
But if AI allows a small team to handle work that previously required several specialists, why would we keep organizing that work in exactly the same way?
The opportunity isn't just to make existing teams more productive. It's to question why we've organized the work this way in the first place.
And that's a much bigger change than introducing another AI tool.
Source: Nik Storonsky, Co-founder & CEO of Revolut, in conversation with David Senra
At Revolut, AI is already changing how work gets done. According to Storonsky, 90% of customer support is automated, and engineering productivity has increased by 30%.
But his next ambition goes yees with their own email, Slack accounts, access to internal systems, and the ability to join meetings and actually do the work.
Today, we divide work between departments, roles, and specialists. We build reporting lines, handovers, and coordination mechanisms around those divisions.
But if AI allows a small team to handle work that previously required several specialists, why would we keep organizing that work in exactly the same way?
The opportunity isn't just to make existing teams more productive. It's to question why we've organized the work this way in the first place.
And that's a much bigger change than introducing another AI tool.
Source: Nik Storonsky, Co-founder & CEO of Revolut, in conversation with David Senra
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55
Europe doesn't need to win the race for the best AI model to build a strong AI economy.
Jensen Huang makes a compelling argument at NVIDIA GTC Berlin.
He describes AI as a five-layer industry: energy, computing, AI factories, models, and applications.
Energy powers the ers run inside AI factories that generate intelligence through models. And those models become useful when they're integrated into products, services, and business processes.
Each layer depends on the others. And each creates opportunities to build businesses, develop capabilities, and capture economic value.
This is particularly relevant for Europe.
We have world-class industrial engineering, energy technology, and manufacturing expertise. Companies like Siemens Energy and Schneider Electric already contribute to the infrastructure AI depends on.
Yet much of our discussion is about catching up with American AI models.
I think that's too narrow.
The question isn't whether Europe can produce its own version of OpenAI. It's where Europe can build capabilities that others will depend on.
That requires more than funding AI startups or encouraging companies to adopt AI tools. It means making deliberate choices about which parts of this new industry we want to build and where our existing strengths give us an advantage.
Europe has a real opportunity here.
But only if we stop treating AI primarily as software we need to adopt and start treating it as an industry we can help shape.
Source: Jensen Huang, NVIDIA GTC Berlin 2026
Jensen Huang makes a compelling argument at NVIDIA GTC Berlin.
He describes AI as a five-layer industry: energy, computing, AI factories, models, and applications.
Energy powers the ers run inside AI factories that generate intelligence through models. And those models become useful when they're integrated into products, services, and business processes.
Each layer depends on the others. And each creates opportunities to build businesses, develop capabilities, and capture economic value.
This is particularly relevant for Europe.
We have world-class industrial engineering, energy technology, and manufacturing expertise. Companies like Siemens Energy and Schneider Electric already contribute to the infrastructure AI depends on.
Yet much of our discussion is about catching up with American AI models.
I think that's too narrow.
The question isn't whether Europe can produce its own version of OpenAI. It's where Europe can build capabilities that others will depend on.
That requires more than funding AI startups or encouraging companies to adopt AI tools. It means making deliberate choices about which parts of this new industry we want to build and where our existing strengths give us an advantage.
Europe has a real opportunity here.
But only if we stop treating AI primarily as software we need to adopt and start treating it as an industry we can help shape.
Source: Jensen Huang, NVIDIA GTC Berlin 2026
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73
Why would a manager approve a risky technology migration when doing nothing is the safer career choice?
Nik Storonsky, CEO of Revolut, points to a problem in traditional banking that goes far beyond outdated technology.
The people who have the authority to replace legacy technical expertise to evaluate how it could be done. And when something goes wrong, they're the ones who have to answer for it.
So they keep the old system.
This is why I believe so many transformations get stuck. We give decision-making authority to people who are far removed from the actual work, reward them for maintaining stability, and then expect them to drive innovation.
You cannot expect different outcomes while keeping the same decision-making structures and incentives.
Give the people who understand the technology a real say in the decisions. And hold management accountable for the cost of doing nothing, not just the consequences of taking action.
Source: Nik Storonsky, Co-founder & CEO of Revolut, in conversation with David Senra
#Leadership #Innovation #OrganizationalChange
Nik Storonsky, CEO of Revolut, points to a problem in traditional banking that goes far beyond outdated technology.
The people who have the authority to replace legacy technical expertise to evaluate how it could be done. And when something goes wrong, they're the ones who have to answer for it.
So they keep the old system.
This is why I believe so many transformations get stuck. We give decision-making authority to people who are far removed from the actual work, reward them for maintaining stability, and then expect them to drive innovation.
You cannot expect different outcomes while keeping the same decision-making structures and incentives.
Give the people who understand the technology a real say in the decisions. And hold management accountable for the cost of doing nothing, not just the consequences of taking action.
Source: Nik Storonsky, Co-founder & CEO of Revolut, in conversation with David Senra
#Leadership #Innovation #OrganizationalChange
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32
AI should raise our expectations of people, not lower them.
That's what I take away from Andy Jassy's thoughts on AI and education.
He encourages his own kids to use AI, experiment with it, and build things. Neither of them was a developer, yet both have built applications with his approach. Trying to keep AI out of universities or workplaces makes no sense.
But his business school example is particularly interesting.
Students used to spend hours reading cases, researching companies, and preparing their analysis. AI can now do much of that work and get everyone to a much better starting point.
So what should we expect from people once they're there?
I'd want to know how they connect the information, what conclusions they draw, and whether they can explain and defend their decisions.
The same applies at work.
If AI takes care of much of the preparation, we should spend less time evaluating how well someone presents information and more time understanding how they think.
When everyone has access to better answers, the quality of our thinking becomes even more important.
Source: Andy Jassy, CEO of Amazon, via a16z on X
That's what I take away from Andy Jassy's thoughts on AI and education.
He encourages his own kids to use AI, experiment with it, and build things. Neither of them was a developer, yet both have built applications with his approach. Trying to keep AI out of universities or workplaces makes no sense.
But his business school example is particularly interesting.
Students used to spend hours reading cases, researching companies, and preparing their analysis. AI can now do much of that work and get everyone to a much better starting point.
So what should we expect from people once they're there?
I'd want to know how they connect the information, what conclusions they draw, and whether they can explain and defend their decisions.
The same applies at work.
If AI takes care of much of the preparation, we should spend less time evaluating how well someone presents information and more time understanding how they think.
When everyone has access to better answers, the quality of our thinking becomes even more important.
Source: Andy Jassy, CEO of Amazon, via a16z on X
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27
“No work gets done in the CEO's office.”
Strauss Zelnick, Chairman and CEO of Take-Two Interactive, has a refreshingly clear view of his job: agree on the mission, set the strategy, shape the culture, and make sure the organization delivers.
The actual work happens through ees his role as enabling them to succeed and stepping in when a problem genuinely requires his involvement.
This resonates with me because I used to be the bottleneck in my own company. Too many decisions came through me, and the more I got involved, the more dependent the organization became on me. I thought I was helping. In reality, I was slowing things down.
One of the biggest lessons I've learned as a founder is that being accountable doesn't mean being involved in everything.
People need clarity about where we're going, the authority to make decisions, and the confidence that they don't need my approval for every step.
My job isn't to have all the answers. It's to build an organization that doesn't need me to provide them.
Source: Strauss Zelnick in conversation with David Senra.
Strauss Zelnick, Chairman and CEO of Take-Two Interactive, has a refreshingly clear view of his job: agree on the mission, set the strategy, shape the culture, and make sure the organization delivers.
The actual work happens through ees his role as enabling them to succeed and stepping in when a problem genuinely requires his involvement.
This resonates with me because I used to be the bottleneck in my own company. Too many decisions came through me, and the more I got involved, the more dependent the organization became on me. I thought I was helping. In reality, I was slowing things down.
One of the biggest lessons I've learned as a founder is that being accountable doesn't mean being involved in everything.
People need clarity about where we're going, the authority to make decisions, and the confidence that they don't need my approval for every step.
My job isn't to have all the answers. It's to build an organization that doesn't need me to provide them.
Source: Strauss Zelnick in conversation with David Senra.
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45
Preparing for an interview can reveal where your own thinking is still unclear.
Can you explain a decision? Defend an assumption? Reconsider it when someone challenges you?
These are skills I value well beyond an interview.
Lenny Rachitsky curated seven resources for PM ecisions. We’ve collected them in this video:
1. Insider Loops: company-specific interview preparation
2. Never Search Alone: peer support for your job search
3. AI Practice Copilot: practice with feedback
4. Ultraspeaking: clearer communication under pressure
5. How to use AI for your next job interview: a practical coaching workflow
6. The definitive guides: product sense and analytical thinking
7. Vocation: direction for your next career move
I’d start with one, practise, and use the feedback to understand where my reasoning needs work. That is where these resources become useful.
Links to all seven resources are in the first comment.
Source: Lenny Rachitsky (@lennysan), X.
#ProductManagement #Learning #CareerDevelopment #AgileAcademy
Can you explain a decision? Defend an assumption? Reconsider it when someone challenges you?
These are skills I value well beyond an interview.
Lenny Rachitsky curated seven resources for PM ecisions. We’ve collected them in this video:
1. Insider Loops: company-specific interview preparation
2. Never Search Alone: peer support for your job search
3. AI Practice Copilot: practice with feedback
4. Ultraspeaking: clearer communication under pressure
5. How to use AI for your next job interview: a practical coaching workflow
6. The definitive guides: product sense and analytical thinking
7. Vocation: direction for your next career move
I’d start with one, practise, and use the feedback to understand where my reasoning needs work. That is where these resources become useful.
Links to all seven resources are in the first comment.
Source: Lenny Rachitsky (@lennysan), X.
#ProductManagement #Learning #CareerDevelopment #AgileAcademy
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Here are all 7 resources from Lenny Rachitsky's list:
Insider Loops – PM interview preparation
insiderloops.com/?utm_source=le…
Never Search Alone – Community-based job search support
neversearchalone.org/?ref=lennysjobs
AI Practice Copilot – AI-powered interview practice
benerez.com/copilot/lenny
Ultraspeaking – Communication and speaking practice
ultraspeaking.com/lennyslist/?vi…
How to Use AI for Your Next Job Interview
lennysnewsletter.com/p/how-to-use-a…
The Definitive Guide to Mastering Product Sense and Analytical Thinking Interviews
Part 1: lennysnewsletter.com/p/the-definiti…
Part 2: lennysnewsletter.com/p/the-definiti…
Vocation – Explore your next career move
joinvocation.com/?utm_source=le…
Credit: Lenny Rachitsky
Save this list for your next PM interview.
Insider Loops – PM interview preparation
insiderloops.com/?utm_source=le…
Never Search Alone – Community-based job search support
neversearchalone.org/?ref=lennysjobs
AI Practice Copilot – AI-powered interview practice
benerez.com/copilot/lenny
Ultraspeaking – Communication and speaking practice
ultraspeaking.com/lennyslist/?vi…
How to Use AI for Your Next Job Interview
lennysnewsletter.com/p/how-to-use-a…
The Definitive Guide to Mastering Product Sense and Analytical Thinking Interviews
Part 1: lennysnewsletter.com/p/the-definiti…
Part 2: lennysnewsletter.com/p/the-definiti…
Vocation – Explore your next career move
joinvocation.com/?utm_source=le…
Credit: Lenny Rachitsky
Save this list for your next PM interview.
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The biggest competitive advantage in AI might be imagination.
Marc Andreessen describes a gap between AI-native startups and established companies that he says is wider than anything he's seen in his career.
And it's growing.
What interests me isn't just that some founders are s that they're imagining entirely different businesses.
Products that would have required hundreds of engineers. Companies that combine software, hardware, and services. Business models that would have seemed unrealistic only a few years ago.
AI is changing what a small team can realistically attempt.
And that creates a challenge for established companies.
Their biggest disadvantage may not be access to technology.
It's the assumptions they bring to the table.
When you've spent years building a successful business around a particular product, market, or operating model, it's difficult to imagine a future where those boundaries no longer matter.
Of course, imagining something doesn't mean you can build a successful business around it.
But if Andreessen is right, the next generation of competitors may not just execute existing ideas faster.
They may pursue ideas that established companies haven't even considered.
Source: Marc Andreessen, a16z.
#AI #Innovation #Entrepreneurship
Marc Andreessen describes a gap between AI-native startups and established companies that he says is wider than anything he's seen in his career.
And it's growing.
What interests me isn't just that some founders are s that they're imagining entirely different businesses.
Products that would have required hundreds of engineers. Companies that combine software, hardware, and services. Business models that would have seemed unrealistic only a few years ago.
AI is changing what a small team can realistically attempt.
And that creates a challenge for established companies.
Their biggest disadvantage may not be access to technology.
It's the assumptions they bring to the table.
When you've spent years building a successful business around a particular product, market, or operating model, it's difficult to imagine a future where those boundaries no longer matter.
Of course, imagining something doesn't mean you can build a successful business around it.
But if Andreessen is right, the next generation of competitors may not just execute existing ideas faster.
They may pursue ideas that established companies haven't even considered.
Source: Marc Andreessen, a16z.
#AI #Innovation #Entrepreneurship
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Just because AI can replace a job doesn't mean replacing it is the right goal.
I think this is one of the most important questions in the current AI debate.
Tristan Harris argues that we're putting enormous effort into building AI that can do everything humans can do, while tion to what we actually want that technology to achieve.
If AI allows you to do the same work with half the people, that's an attractive business case. At least on paper.
But what happens if you use the same technology to help those people do things they couldn't do before?
Serve more customers. Solve problems that previously seemed too expensive. Create products and services that weren't economically viable.
I'm not arguing that every job can or should be preserved. Technology has always changed the nature of work.
But I don't want us to assume that reducing headcount is the natural measure of success when introducing AI.
Of course, businesses have to remain economically viable. But within those constraints, leaders still have choices about what they want AI to achieve.
And I think we should be having that conversation before we start automating everything we can.
Source: Tristan Harris on The Diary of a CEO with Steven Bartlett.
#AI #Leadership #FutureOfWork
I think this is one of the most important questions in the current AI debate.
Tristan Harris argues that we're putting enormous effort into building AI that can do everything humans can do, while tion to what we actually want that technology to achieve.
If AI allows you to do the same work with half the people, that's an attractive business case. At least on paper.
But what happens if you use the same technology to help those people do things they couldn't do before?
Serve more customers. Solve problems that previously seemed too expensive. Create products and services that weren't economically viable.
I'm not arguing that every job can or should be preserved. Technology has always changed the nature of work.
But I don't want us to assume that reducing headcount is the natural measure of success when introducing AI.
Of course, businesses have to remain economically viable. But within those constraints, leaders still have choices about what they want AI to achieve.
And I think we should be having that conversation before we start automating everything we can.
Source: Tristan Harris on The Diary of a CEO with Steven Bartlett.
#AI #Leadership #FutureOfWork
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What if AI makes the office more important, not less?
David Vélez, Founder and CEO of Nubank, makes an argument I can relate to.
We've spent years discussing how technology allows us to work from anywhere. But perhaps we haven't spent enough time thinking about what we lose other.
I believe in giving people the freedom to decide how and where they work. But I also think there's something valuable about regularly spending time together.
Some conversations simply don't happen when every interaction has to be scheduled. And a lot of learning happens when people work alongside each other, ask questions, and exchange ideas without a formal meeting.
That's difficult to replicate entirely through a screen.
I'm not advocating for five days a week in the office. But I do think we've sometimes underestimated the value of being physically together.
And as AI changes the way we work, that value might actually increase.
Source: David Vélez on Invest Like the Best with Patrick O'Shaughnessy.
#AI #FutureOfWork #Leadership
David Vélez, Founder and CEO of Nubank, makes an argument I can relate to.
We've spent years discussing how technology allows us to work from anywhere. But perhaps we haven't spent enough time thinking about what we lose other.
I believe in giving people the freedom to decide how and where they work. But I also think there's something valuable about regularly spending time together.
Some conversations simply don't happen when every interaction has to be scheduled. And a lot of learning happens when people work alongside each other, ask questions, and exchange ideas without a formal meeting.
That's difficult to replicate entirely through a screen.
I'm not advocating for five days a week in the office. But I do think we've sometimes underestimated the value of being physically together.
And as AI changes the way we work, that value might actually increase.
Source: David Vélez on Invest Like the Best with Patrick O'Shaughnessy.
#AI #FutureOfWork #Leadership
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