In order to encourage our employees like Kari and their managers like Jack to shift their mind-set in the direction of experimentation, we use the image of placing bets. This motivates employees to think of themselves as entrepreneurs—who typically don’t succeed without some failures. The examples of Kari and (from a few pages back) Paolo reflect everyday life at Netflix. We want all employees taking bets they believe in and trying new things, even when the boss or others think the ideas are dumb. When some of those bets don’t pay off, we just fix the problems that arise as quickly as possible and discuss what we’ve learned. In our creative business, rapid recovery is the best model.

The big guys, Neil and Reed, were set against the idea publicly and privately. At most places this would be end of discussion. But Todd Yellin, VP of product, (who worked for Neil) had doubts. He discussed with Zach Schendel (senior user experience researcher) about running some tests to find out if Neil and Reed’s claims were accurate. Zach remembers it like this:

I thought, “Neil and Reed are against this idea. Is it okay to test it out?” At any of my past employers, that would not have been a good move. But the lore at Netflix is all about lower-level employees accomplishing amazing things in the face of hierarchical opposition. With that in mind I went ahead.

But when Ted and Melissa sat down in late October 2017, Ted spoke not about what people at Netflix knew but about all the things they didn’t know yet:

Look, Melissa, we are at a turning point for Netflix. We have forty-four million members in the US. The big growth will be international and we have a lot to learn. We don’t know if Saudi Arabians watch more or less TV during Ramadan. We don’t know if Italians prefer documentaries or comedies. We don’t know if Indonesians are more likely to watch movies alone in their bedrooms or around their family televisions. If we are going to succeed, we need to become an international learning machine.

Melissa was already familiar with the language of bet-taking used at Netflix and the implication that some bets will succeed and others will fail. What the gambling analogy didn’t capture was the critical aspect of learning from all that failing. This brings us to the context set by Ted:

As your team purchases and creates content around the world, we need to be laser-focused on learning. We should be ready to take bigger risks in high-growth-potential countries like India or Brazil so that we learn more about those markets. Let’s have some wins. But let’s also have some big messy losses where we learn how to succeed better the next time. We should always be asking, “If we purchase this show and it bombs, what will we learn from that?” If there is something big to learn, let’s go ahead and take the bet.

I was also told that a brand-new CEO shouldn’t be trying to make huge acquisitions. I was “crazy,” as one of our investment bankers put it, because the numbers would never work out and this was an impossible “sale” to the street.

The banker had a point. It’s true that on paper the deal didn’t make obvious sense. But I felt certain that this level of ingenuity was worth more than any of us understood or could calculate at the time. It’s perhaps not the most responsible advice in a book like this to say that leaders should just go out there and trust their gut, because it might be interpreted as endorsing impulsivity over thoughtfulness, gambling rather than careful study. As with everything, the key is awareness, taking it all in and weighing every factor—your own motivations, what the people you trust are saying, what careful study and analysis tell you, and then what analysis can’t tell you. You carefully consider all of these factors, understanding that no two circumstances are alike, and then, if you’re in charge, it still ultimately comes down to instinct. Is this right or isn’t it? Nothing is a sure thing, but you need at the very least to be willing to take big risks. You can’t have big wins without them.

SEVENTEEN: Money

“Like many firms, Nvidia allowed employees to purchase stock at a discount to market prices. What set Nvidia’s program apart was that employees were allowed to purchase stock at a discount to the lowest price at any point in the last two years. These purchases were capped at a certain dollar amount, but as the stock went vertical, the program basically turned into free money, and those who maxed out their contributions each year made the trade of a lifetime. With the windfall extending deep into middle management, some newer employees expressed concerns that the nouveau-riche veterans were entering a state of “semiretirement.” Executives disputed this characterization. Jeff Fisher, who ran the company’s gaming side, had been among the first thirty employees. “Many of us are financial volunteers at this point,” he said, “but we believe in the mission.”

The lure of developing this revolutionary technology offered purpose beyond what money could buy. This was especially true of the old guard, who’d spent years explaining to baffled peers why they were working for a gaming company and who constantly had to correct the pronunciation of the firm’s name. AI had not been a consideration for these veterans, and they were as surprised to be working on it as anybody. “There was no way me, or anybody else, could have dreamed at the time that this stuff that science fiction writers might come up with has become a reality,” said Jay Puri, Nvidia’s head of sales, who started work at the company in 2005. The value of Puri’s shares exceeded $700 million by 2024, but he felt that the interesting work at Nvidia was only beginning. “Maybe I’m biased, but I think it really is the most important technology company of our time,” he said.