QUESTMAXXING

What D&D taught me about a theory of the game, shared missions, and leading in the age of AI

For many kids, organized sports offer their earliest lessons in teamwork, practice, and having a theory of the game. For me, it was Dungeons & Dragons.

In 1979, when I was twelve years old, it introduced me to a way of thinking about how individuals interact with each other that continues to inform my life and career. Its lessons would eventually even help me conceptualize LinkedIn. What I learned from D&D steered me toward making early investments in Facebook and Airbnb. It shapes how I think about AI. It’s the reason I advocate for systems that put genuine capability in the hands of individuals — not just government actors or a handful of corporations — but people, acting for themselves and for each other.

With its polyhedral dice and a rulebook that reads as if it were written by a government tax attorney in between bouts of jousting at the Renaissance Faire, D&D is nothing if not complex. But it’s also extremely vivid in how it mechanizes its philosophy of the world. So much so that even a somewhat quiet 12-year-old can begin to decipher the nuances of social literacy.

What, then, did I actually learn in between fending off goblins and making hard decisions about whether or not to choose a 50-foot coil of rope over an extra sack of gold? Four key principles:

1. You have to have a theory of the game

2. A shared mission is a form of network architecture.

3. Learning is a competitive strategy.

4. Every person on the team needs to be the hero of their own story, but those stories should cohere into a shared quest.

Together these principles inform a larger question that has given me a sense of enduring purpose, both personally and in my career: How do individuals become their best selves through their relationships with others? What Dungeons & Dragons helped me recognize, long before I ever considered a career in entrepreneurship, is that effective leaders, and thus effective organizations, spend more time designing roles than writing rules.

That lesson feels newly urgent. AI is unsettling our assumptions about how work gets done and who does it — which raises the stakes on the questions D&D taught me to ask.

YOU HAVE TO HAVE A THEORY OF THE GAME
In many games, the win conditions are stated before anyone takes the playing field or rolls the dice. Have more points on the board than the other team when the clock runs out. Bankrupt every other player. Trap your opponent’s king. The conditions are fixed, identical for everyone, and terminal: as soon as they’re satisfied, it’s game over. With Dungeons & Dragons, none of that’s true. Its rulebook is meticulous about how the world works. It tells you, at length, how far you can move or what a lock costs to pick. It says virtually nothing about what you should be trying to accomplish within the context of the universe it creates.

What D&D has instead are success conditions, nested at every scale. A skill check succeeds or fails. An encounter is survived. A quest is completed. But completing the quest doesn’t end the game. It ends that arc, and then you’re still at the table, with the option to take on a harder challenge if everyone wants to keep playing. At the same time, D&D does have explicit loss conditions, at least at the character level. Your character dies, or everyone in your part dies, and that thread is over, forever. But while individual characters can be knocked out permanently, no victory is permanent in the same way. Which, it turns out, is a far better model of startups, careers, and institutions than what most other games offer.

The deeper difference between D&D and many other games is who sets the conditions. In chess or basketball, they’re handed to you. D&D’s get authored mid-play — by a Dungeon Master responding to what the players actually do, and by the players deciding which of the world’s many problems is theirs. Nothing in the rulebook says it’s important to come to the aid of a fallen prince. That’s something a party decides to do or not do, based on their own prerogatives about what they think is worth doing.

So having a theory of the game in D&D is a major part of the game itself. In the absence of an assigned objective, you construct one, which means forming a hypothesis about what you in this world prefer as rewards, acting on that choice, and finding out whether you were right.

Once you’re constructing objectives yourself, you start realizing the importance of strategy and tactics and the relationship between them. Your theory of the game tells you what the world rewards. Strategy is the standing bet you place on that theory; it informs the kind of character you decide to build, and the team you assemble, and the kinds of problems you go looking for. Tactics, in contrast, are what you do at the bridge with the bandits on it.

And ultimately each informs the other. Strategy tells you what to look for, and what you find tells you whether the strategy was any good. For example, losing a fight you shouldn’t have been in is a strategic error that merely presents as a tactical one. If your theory of the game is wrong, you can execute flawlessly and still fail repeatedly. And, of course, sometimes the key lesson is that you were lucky or unlucky.

On the other hand, occasionally a tactic works so well that it stops being a tactic. You improvise something, or spot a quirk about how the rules interact in a given situation, and it produces results so far out of proportion to its cost that the correct response is to reorganize everything around it. Your strategy becomes: do more of that, and find out how far it goes.

This is why the theory has to stay current. In my case, my theory of the game started evolving as I began to more fully appreciate that the Dungeon Master is a person, not an algorithm. The rules are real, but they’re adjudicated by someone with their own sense of the story, sitting at a table with four other people who want the evening to be interesting. In D&D, any theory that accounts only for the printed mechanics is missing most of the actual system.

Generalize from that, and you get what I mean by a theory of the game: an explicit model of how the environment you’re operating in converts effort into outcomes, and — because no one is going to hand you a win condition — an explicit claim about what would count as success in the first place. What does this environment reward, as opposed to merely permit? Who are the real actors, and what does each of them want? Which constraints are genuinely fixed, and which is everyone just assuming? A good theory is concrete enough to be wrong; that’s one of the things that makes it valuable.

Most people, in most organizations, are running on a theory they never chose. In many cases, they inherited it from a manager, who inherited it from another manager. Or even more broadly, it’s an industry norm, a story about how careers worked in, say, 1955. Work hard, deliver on your assignments, and the right people will notice is a truism — but it’s also a theory. It’s just an implicit, generally unexamined one, which hasn’t been updated or revised, even though the evidence says it should. Instead, it’s just gotten less effective even as the world has changed.

That’s why keeping your theory of the game current is the core part of the job in entrepreneurship and investing. Every company I’ve backed represents a bet on a specific claim about how a market will behave that the market does not yet agree with. The point isn’t to be contrarian for its own sake — that’s just a different way of not thinking. It’s to hold a differentiated model and the evidence for it, and to keep pressure-testing both.

A theory of the game is also the prerequisite for everything that follows. You can’t coordinate a group around a shared mission if each person is playing a different game in their head.

A SHARED MISSION IS A FORM OF NETWORK ARCHITECTURE
A deep sense of purpose is essential to most people; we all want to feel that our individual efforts contribute to something meaningful, lasting, and ideally, larger than ourselves. More broadly, the power of explicitly shared purpose is what animates team sports, political movements, social activism, and, in microcosm, D&D and similar board games.

In Monopoly, you are fundamentally a shark, actively trying to make your friends go bankrupt so you can seize their assets. In poker, you’re an island, keeping your own assets and resources (aka your hand) literally close to your chest while trying to uncover the vulnerabilities of everyone else at the table. One of the reasons I was fortunate to discover D&D early is that it was one of the few games, at least among those I had come across, designed as a team sport. In D&D, you are fundamentally a teammate. You have your individual story, but you are also part of a team. That’s the premise, not a strategy.

In business, we use profitability as a proxy for shared purpose, and it’s one reason capitalism is such an effective platform for organizing human labor and ingenuity and achieving tangible progress at scale. But in the end, “we all love money” is still just a hack, transactional rather than transformational, a shortcut that substitutes external rewards for subjective purpose. In my own experience, I’ve found that “missionary” companies are generally even better than merely “mercenary” ones at operating effectively, growing rapidly, and adapting quickly. Which means they also tend to be better at generating revenues and profits over time.

Why? A dramatic, specifically articulated mission, one that goes beyond making money, gives an organization a unique identity and purpose, and thus every person in that organization a unique collective identity and purpose. Sometimes, a company’s “mission” is written off as “ethics theater”, a way to fill white space on annual reports. But thinking that way is either a lost opportunity or an admission of failure. A mission is only “theater” if it is drafted poorly or operationalized poorly. In such cases, it’s just words, not aspirations and values employees genuinely believe in, or work to make true.

In contrast, a well-defined “mission” is a deeply held ethics argument, and just as importantly, it’s a network and operations argument. A compelling mission gives every person in an organization the same north star, which means that decisions made at every level — about product, culture, hiring, strategy — are more likely to be coherent with each other. It’s coordination infrastructure.

In D&D, this concept is expressed through the quest—the singular, high-stakes objective that transforms individual players (who might in fact be strangers) into a unified party. In one instance of the game, you might be escorting a fallen prince to his throne. In another, preventing a cult from summoning an ancient deity. In every instance, the specific quest becomes the filter for every decision. If the mission is to stop a plague from leveling a city, you don’t waste time haggling over the price of a room at the inn or getting distracted by side-quests. The urgency and clarity of the mission create instant coherence.

When I was an undergraduate at Stanford University, I founded the Symbolic Systems Forum, a student society that brought technology industry figures onto
campus for weekly fireside conversations. While I wouldn’t have described it this way then, there were basically three stakeholders in this process, all with different incentives. First there were the students, who wanted access to accomplished experts in their potential career domains. Then there were the experts, who had limited time and wanted to spend it where they could apply it most valuably. Finally, there was Stanford’s faculty, who had their own ideas about who warranted an invitation, and what kinds of topics and discourse students should be engaging in.

What I recognized pretty quickly was that, even as the founder/leader of the group that was organizing the events, my authority was limited. I couldn’t simply ask a busy executive to show up, and I couldn’t force a tenured professor to approve a speaker they found “intellectually thin.” There was also no guarantee that other students would show up too. Instead, it was a matter of network coordination: How could I help every participant see themselves as actors with specific purposes in a shared frame? The way I saw it, our collective mission was to bridge the gap between the classroom and the “real world” for the next generation of builders.

Once I started emphasizing that frame, the friction didn’t disappear entirely. But it did help the various stakeholders better conceptualize their roles – including seeing how this frame aligned with their own goals and incentives – within the shared mission. And once that template was set, enough people understood themselves to be part of something with growing value that a kind of mini network effect took hold.

Faculty that spent less time gatekeeping and more time mentoring attracted more students. As more students showed up, experts stopped viewing the forum as a chore and saw it as a talent pipeline. Students, in turn, realized that informal access to faculty and experts gave them the opportunity to become more active architects of their own education. As the value of participation compounded for all participants, more students, professors, and experts wanted to participate.

When I co-founded LinkedIn in 2002, I put similar ideas–and ideals–about our company’s shared mission into play. Since our goal was to create the world’s leading platform for helping individuals transform their economic lives through professional relationships, we added the following question to our job interviews: “What’s the job you want after LinkedIn?”

Asking candidates about their exit strategy for a job they haven’t even accepted yet may seem like a pretty questionable recruiting tactic. (”You’re saying I should be thinking about the divorce before you’ve even proposed?”)

In our case, though, we recognized that as a global platform helping people advance their careers across all industries, we couldn’t be credible if we treated our own employees as assets to be guarded and retained, rather than as individuals with their own goals and aspirations, with whom we were in mutually beneficial but not necessarily permanent alliance. For our mission to mean something externally, we also had to live it internally.

The bonus was that this question turned out to be a great way to identify employees who not only were ready to commit to LinkedIn’s mission, but also deeply understood its nuances. The candidates with the strongest answers were already thinking very intentionally and ambitiously about their careers as a kind of narrative with specific engagements, goals, and outcomes at each stage, whether that meant sharpening a skill, developing their own personal networks, or cultivating the kind of reputational capital that would enable their next big leap. They had an explicit theory of the game for themselves, and that helped them understand, at a time when a lot of people could not see past existing concepts like job boards (aka Monster.com) or contact software (aka ACT!) that we were really a much more expansive and persistent platform for professional identity, network development, and career advancement time.

Naturally, as soon as we hired these candidates, we also encouraged them to post profiles on LinkedIn if they didn’t already have them. This was far from standard operating procedure for any company in the early 2000s, much less for Silicon Valley start-ups where intense talent wars were always a fact of life. But we wanted our employees to derive direct value from the platform they were helping to create. We wanted potential partners and investors to see the players we’d recruited. We wanted other companies to recognize that encouraging their own employees to create profiles was a show of strength rather than something they should try to prohibit.

LEARNING AS COMPETITIVE STRATEGY
It’s hard to find anyone who will argue that learning is a bad thing. We treat “lifelong learning” as a corporate piety, right up there with “integrity” and “innovation.” We treat it as a kind of multi-vitamin. Most everyone agrees it’s generally “good for you”—but for what specific purpose?

Once again, Dungeons & Dragons was instructive for me. In the late 1970s, when I started playing, the core rulebook came in three volumes and was already 112 pages. For a game, that was a monumental ask; it even made the Treasury Department’s 1040 instructions seem relatively concise. (Today, just the Player’s Handbook is nearly 400 pages long. And to really play, you need the Dungeon Master’s Guide and Monster Manual as well, which are equally long.)

However, I recognized early on that if you wanted to excel at D&D you had to RTFM. That’s because ‘knowledge’ isn’t abstract in the world it creates. It’s the code that gives you the power to tilt the odds of success in your favor. If you pay close attention to the manual, you learn how a specific combination of a ‘Bless’ spell and using arrow slits for 90% cover can turn an impossible battle into a cakewalk. You discover that a ‘10-foot pole’ isn’t just an item on a list—it’s a low-cost diagnostic tool for identifying lethal traps before they cost you the mission. In this environment, deep regulatory fluency is a massive competitive advantage.

But in the real world, as I suggested above, people often work implicit, unquestioned, outmoded theories of the game, especially in corporate settings defined by legacy OKRs, arbitrary benchmarks, and other perverse incentives that end up prioritizing processes over the mission. And in these domains, learning as a rigorously integrated and applied competitive strategy, learning as a live and continuously updated theory of the game, often falls by the wayside.

Instead, companies often offer “professional development” or “training programs” that are, at best, only tangentially connected to specific, high-stakes organizational goals. Truly effective leadership requires building an organization that learns faster than its environment changes, and—crucially—possesses a culture capable of implementing that learning in real-time.

This may sound like a truism, but that’s exactly why implementation is everything. One way to implement effectively is to “gamify” learning within your organization.

For example, I was at LinkedIn in 2007 when Facebook launched its developer platform. At the time, the mood was dire. Many commentators concluded that LinkedIn was effectively finished. The assumption was that our professional network would simply be rebuilt as an app on top of Facebook, rendering us a mere “Side Quest.”

Instead of accepting this narrative, I designed a “Red Team” experiment. I assembled some of our most talented people, gave them every bit of proprietary logic and competitive intelligence we had, and gave them a singular quest: Destroy LinkedIn using the Facebook platform. They tried. And they failed.

That failure was our first data point. It proved that LinkedIn’s “moat” was deeper than we’d believed. We then pivoted to a new quest: Determine exactly what the Facebook platform was actually good for. We built an experimental app that became the third-most-installed app on Facebook for ten weeks. Having stress-tested the competition, the team returned to our core product with an experience-driven degree of mastery we wouldn’t have got from just holding meetings about the challenge we were facing. Instead, this was learning culture in practice, a structured organizational response to uncertainty. Because of these efforts, we gained insights we could now effectively apply moving forward.

Unfortunately, the clarity of mission is not always at the level it was in that instance. At another point in LinkedIn’s development, I launched LinkedIn Groups as a defensive response to competitors who were building group-based products.

But we didn’t see it as the same existential threat as we saw Facebook’s developers platform, so we didn’t have a defined mission. That in turn impacted both the nature of our learnings from LinkedIn Groups, and how we acted on that learning. Basically, we got stuck in a middle ground of not investing resources and commitment into making it work, but also not killing it when it wasn’t working. When we learned it wasn’t working in its current form, we might have responded by killing it quickly. That would have been learning as a competitive strategy. Instead, we simply kept doing what we were doing, with the Groups product consuming resources while generating limited value.

This is why it’s so essential to make explicit decisions about the experiments you want to run. Before you take on any quest, you should scope out what you hope to learn, and how you will apply whatever you actually do learn. Otherwise, you’ll probably just be devoting resources toward creating organizational ambiguity. And in organizations, ambiguity about which projects matter — which paths lead to winning, and which ones lead to losing — tends to be profoundly demoralizing. People need to feel as if they are genuinely on a path to something, not just going through the motions. Which brings me back to D&D….

HEROES WORKING TOGETHER ACCOMPLISH INCREDIBLE FEATS
I have always believed that people working together can achieve a kind of emergent intelligence that far outstrips the sum of its parts. However, it’s a mistake to think individuals must become interchangeable to achieve team unity. Far from it: I believe that emphasizing the individual not just as a role player, but as a player with a “heroic” dimension, is crucial. It’s what provides the essential spark of purpose and identity.

In Dungeons & Dragons, this is the very soul of the game. You don’t show up to the table to be “Unit 4” or even Junior Marketing Coordinator. You show up to be Thrain the Dwarven Cleric or Elara the Elven Ranger.

Using the terminology of “heroes” creates a specific set of narrative expectations: a beginning and an end, a path of progress, and a series of challenging trials. So ultimately this copy choice goes beyond just a communications strategy and becomes a kind of ontological claim. The story an individual tells themselves about what she is doing—and the story a group tells itself about its shared project—determines their resilience. It determines whether they can sustain effort when things get tough, absorb setbacks, and feel that their time is being well spent.

But a narrative is only as strong as the parties that inhabit it. And narratives are never static – at least not in adventure games like D&D…and real life. In these domains, narratives are constantly evolving. Which is precisely why it’s so valuable to think in terms of theories of the game; shared quests as network architecture; learning as a competitive strategy; and individual participants as heroes. Because working together, they all compound.

As narratives evolve due to unforeseen circumstances, the outcomes various actions produce, and shifting motivations, theories of the game get empirically tested and potentially re-hypothesized. A strong shared quest ensures that player alignment persists. Effective learning keeps the team adaptive and on-course. Heroic identities keep roles clear and morale high. If one element of this dynamic is missing, performance declines. If two or more are missing, the possibility of a satisfactory ending drops to zero.

And leadership in this context? In D&D, as in startups, a “Heroic Collective” is a delicate balance. The Fighter and the Wizard have entirely different skills, different gear, and entirely different styles of engagement. But while they might not share the same motivations, they are on the same quest. Because no single member can complete the quest alone, the leader isn’t the person who tells everyone what to do. The leader is the person who keeps the quest alive and ensures every member can see their own heroic arc within it, and also encourages everyone else to display leadership too, in different contexts. In this way, all participants collectively advance the story so that it remains true, compelling, and inclusive enough to hold the group together.

THE STRESS TEST
How does this work in the AI era? As AI systems increase their capacity to work autonomously in increasingly complex environments, there are growing concerns that we humans may be automating ourselves out of the loop entirely. As I explored at length in Superagency, I believe we can move toward a much different future than that if we make the right choices.

In this future, people use AI in ways that augment their own capabilities and collaborate more effectively with co-workers and partners. Anyone who uses AI in this manner needs leadership skills, because they’ll be leveraging AI agents in ever-widening contexts. You might have one team for one project, another team for a second, an AI chief of staff coordinating those two teams, etc. And many human colleagues will remain in the mix too.

That’s why I believe the lessons D&D taught matter more than ever. When billions of people have access not just to unlimited knowledge, but also expert-level entities capable of using that knowledge in intelligent ways, competitive advantage resides in networks.

Who has the power to adapt to rapid shifts in the landscape most productively and distribute ideas fastest, with reliable precision? Whoever has the most robust and finely tuned networks. Intelligence is the table stakes, distribution is key, but alignment is how you yield value and gain competitive advantage from the first two variables.

Given that many players moving forward will be AIs, the challenge of maintaining coherence becomes both more acute and more interesting. An AI agent typically operates around an overriding objective function — a fixed target it’s optimizing toward. Grant it autonomy, and it may pursue that objective in ways that drift from the broader mission, cutting corners you didn’t anticipate, or “winning” in ways that feel like losing. But that same autonomy is precisely what makes an agent powerful. It’s not just tireless execution — it’s the capacity to learn, adapt, and discover solutions no human would have thought to try.

A leader’s job, then, is less like conducting a symphony orchestra performing a score and more like leading a jazz ensemble. The musicians are improvising — responding to each other, to the room, to the moment — but still working together in a way that’s recognizably harmonious. Your role is to set the key, hold the tempo, and know when a soloist is riffing brilliantly versus when they’ve wandered off into noise.

In addition, the fact that intelligence is being increasingly commoditized in the AI age does not mean that it will no longer be valuable. Nor will it decrease the value of learning as a competitive advantage.

In fact, the opposite is true. It’s a bit like Bitcoin mining where, in order to validate transactions on the network, millions of machines around the world compete to solve extremely complex cryptographic hashes every ten minutes or so. This process — known as proof-of-work — is what secures the entire Bitcoin ledger; each solved hash confirms a new block of transactions and makes the historical record exponentially harder to tamper with. The reward for winning is newly minted Bitcoin, plus the transaction fees bundled into that block, a direct financial incentive that keeps millions of machines honest and the network trustworthy.

On the one hand, actually being the first to solve the hash is akin to winning the lottery. In this race, every finish is a photo finish, because dozens or even hundreds of miners arrive at the correct answer within milliseconds of each other, and only the one whose solution reaches the network a fraction of a second faster claims the prize. But obviously it’s not completely a matter of chance. You still need the intelligence to solve the hash.

In the AI era, every market, every opportunity, every competitive arena is becoming more contested on a global basis. The compute, the models, the raw intelligence — these are rapidly becoming table stakes, available to any well-resourced player. What remains scarce, and therefore decisive, is the ability to learn faster than your environment changes, to synthesize that learning into coherent strategy, and to move with enough conviction and speed that your insights compound before your competitors catch up.

It’s also true that AIs are already learning faster than we are. But this doesn’t preclude human utility. At the highest level, quests require subjective value. An AI can optimize for a variable, but it cannot “want” an outcome. Even if you give an AI a broad mandate — “Take this $5,000 and turn it into $1 million” — it will make choices based on probability, not purpose.

In Dungeons & Dragons, Dungeon Masters don’t choose quests because they’re “mathematically optimal.” They choose them because they recognize they’re meaningful to the players.

At this pivotal moment, on this count, I believe the global demand for leadership is near inexhaustible. Because the best path toward retaining human agency in the age of AI isn’t about stopping the clock or limiting the “rulebook.” Throughout history, new technologies have always brought new challenges along with new opportunities and new solutions. Defining, then expanding, then re-defining what it means to be human through technological innovation is the story of our species.

And what AI gives us, just like fire, and the printing press, and electricity before it, is the opportunity to build stronger networks, and deploy more intelligence, and attempt increasingly ambitious and complex and fulfilling quests. Through a D&D lens, the rulebook grows ever more expansive now. The kinds of teams and networks you can assemble follows suit.

So what stories do you want to tell? What quests are worth your time, your expertise, and, most of all, your aspirations about who you want to become?