Ambition: version 8♦ is now out.

The latest rules for Ambition are here. Mostly, the changes are simplifications to the scoring system in order to increase the game’s mass appeal and, hopefully, virality. I don’t believe that I’ve compromised on the game design in doing so; the intrinsic strategic complexity remains, but the cognitive overload associated with the scoring system has been trimmed back a bit.

I’m planning, after years of delinquency on this matter, to release an online version late this year, but I’ve wanted to get the rules to a steady state before doing so. This iteration, I am pretty sure, is or is near the final one, at least as far as the core rules go. There are a few unresolved questions that I have about the scoring system, but I’m going to wait until I have real data (from players, not simulations) before making those calls.

The roadmap from here looks like this. Currently, I’m working on a command-line console version– an embarrassing minimal viable product (“MVP”)– that I plan on releasing this August. The source code will be on Github and open-source; card games themselves are rather trivial to implement, so there’s no point in hiding that code. The first iteration will be a tutorial (with players making random legal moves) more than a game, designed with the intent of helping people learn Ambition interactively rather than from a dry rules document.

After “launching” this MVP, the next project will be to create real players for a single-player version. As for AI, I have a machine-learning approach in mind that I think will work. That might take a month or two (since this is purely a weekend side project) to implement and run, but I’d like to have that together by mid-autumn. This means there should be real AI players. I have no idea whether they’ll be any good at the game. I may crowd-fund this by creating a KickStarter project to solve the AI problem and giving a percentage away to the person who writes the best player.

After that, I’ll start working on the front-end (like, a real app) of the game, noting that most people are not interested in downloading a command-line card game, and also that people prefer to play against real people rather than AI. I’ve been doing back-end software for my whole career so I have no idea what that will entail or how difficult it will be, but I look forward to the learning experience.

Six languages to master.

Eric Raymond, in “How to Become a Hacker“, recommended five languages: C, Java, Python, Perl, and Lisp. Each he recommended for different reasons: Python and Java as introductory languages, C to understand and hack Unix, Perl because of its use in scripting, and Lisp for, to use his words which are so much better than anything I can come up with, the profound enlightenment experience you will have when you finally get it. That experience will make you a better programmer for the rest of your days, even if you never actually use LISP itself a lot.

It’s 2012. Many languages have come to the fore that didn’t exist when this essay was written. Others, like Perl, have faded somewhat. What is today’s five-language list? I won’t pretend that my answer is necessarily the best; it’s biased based on what I know. That said, I’d think the 5 highest-return languages for people who want to become good engineers are the following, and in this order: Python, C, ML, Clojure, and Scala.

Why these 5? Python I include because it’s easy to learn and, in-the-small, extremely legible. I’d rather not use it for a large system, but people who are just now learning to program are not going to be writing huge systems. They’re not going to be pushing major programs into production. At least, they shouldn’t be. What they should be able to do is scrape a webpage or build a game or investigate an applied math problem and say, “Shit, that’s cool.” Python gets people there quickly. That will motivate them to get deeper into programming. Python is also a language that is not great at many things, but good at one hell of a lot of them. It’s quick to write, legible in the small, and expressive. It allows imperative and functional styles. It has great libraries, and it has strong C bindings, for when performance is needed.

People who are getting started in programming want to do things that are macroscopically interesting from a beginner’s perspective. They don’t just want to learn about algorithms and how compilers work, because none of that’s interesting to them until they learn more of the computer science that motivates the understanding of why these things are important. Compilers aren’t interesting until you’ve written programs in compiled languages. At the start, people want to be able to write games, scrape webpages, and do simple systems tasks that come up. Python is good because it’s relatively easy to do most programming tasks in it.

After Python, C is a good next choice, and not because of its performance. That’s largely irrelevant to whether it will make someone a better programmer (although the confidence, with regard to understanding performance, that can come with knowing C is quite valuable). C is crucial because there’s a lot of computer science that becomes inaccessible if one sticks to higher-level languages (and virtual machines) like Java, C#, and Python. Garbage collection is great, but what is the garbage collector written in? C. As is Unix, notably. For all this, I think C is a better choice than C++ because there’s another important thing about C: C++ is a mess and it’s not clear whether it’s a good language for more than 1% of the purposes to which it’s put, but C, on the other hand, has utterly dominated the mid-level language category. For all its flaws, C is (like SQL for database query languages) a smashing, undisputed success, and for good reasons. The high-level language space is still unsettled, with no clear set of winners, but the midlevel language used to write the runtimes and garbage collectors of those high level languages is usually C, and will be for some time.

Python and C give a person coverage of the mid- and very-high levels of language abstraction. I’m avoiding including low-level (i.e. assembly) languages because I don’t think any of them have the generalist’s interest that would justify top-5 placement. Familiarity with assembly language and how it basically works is a must, but I don’t think mastery of x86 intricacies is necessary for most programmers.

Once a programmer’s fluent in Python and C, we’re talking about someone who can solve most coding problems, but improvement shouldn’t end there. Taste is extremely important, and it’s lack of taste rather than lack of intellectual ability that has created the abundance of terrible code in existence. Languages can’t inherently force people to learn taste, but a good starting point in this direction is ML: SML or OCaml with the “O” mostly not used.

ML has been described as a “functional C” for its elegance. It’s fast, and it’s a simple language, but its strong functional programming support makes it extremely powerful. It also forces people to program from the bottom up. Instead of creating vague “objects” that might be hacked into bloated nonsense over the lifespan of a codebase, they create datatypes (mostly, records and discriminated unions, with parameterized types available for polymorphism) out of simpler ones, and use referentially transparent functions as the basic building blocks of most of their programs. This bottom-up structure forces people to build programs on sound principles (rather than the vague, squishy abstractions of badly-written object-oriented code) but ML’s high-level capability brings people into the awareness that one can write complex software using a bottom-up philosophy. Python and C teach computer science at higher and lower levels, but ML forces a programmer to learn how to write good code.

There’s also something philosophical that Python, C, and Ocaml tend to share that C++ and Java don’t: small-program philosophy, which is generally superior. I’ve written at length about the perils of the alternative. In these languages, it’s much more uncommon to drag in the rats’ nest of dependencies associated with large Java projects. For an added bonus, you never have to look at those fucking ugly singleton directories called “com”. Once a person has used these three languages to a significant extent, one gets a strong sense of how small-program development works and why immodular, large-project orientation is generally a bad thing.

When you write C or Ocaml or Python, you get used to writing whole programs that accomplish something. There’s a problem, you solve it, and you’re done. Now you have a script, or a library, or a long-running executable. You may come back to it to improve it, but in general, you move on to something else, while the solution you’ve created adds to the total stored value of your code repository. That’s what’s great about small-program development: problems are actually solved and work is actually “done” rather than recursively leading to more work without any introspection on whether the features being piled on the work queue make sense. Developers who only experience large-program development– working on huge, immodular Java projects in IDEs a million metaphorical miles from where the code actually runs for real– never get this experience of actually finishing a whole program.

Once a person has grasped ML, we’re talking about a seriously capable programmer, even though ML isn’t a complex language. Learned in the middle of one’s ML career is a point to which I’ll return soon, but for now leave hanging: types are interesting. One of the most important things to learn from ML is how to use the type system to enforce program correctness: it generates a massive suite of implicit unit tests that (a) never have to be written, and (b) don’t contribute to codebase size. (Any decent programmer knows that “lines of code” represent expenditure, not accomplishment.)

The fourth language to learn is Clojure, a Lisp that happens to run on the JVM. The JVM has its warts, but it’s powerful and there are a lot of good reasons to learn that ecosystem, and Clojure’s a great entry point. A lot of exciting work is being done in the JVM ecosystem, and languages like Clojure and Scala keep some excellent programmers interested in it. Clojure is an excellent Lisp, but with its interactive “repl” (read-eval-print-loop) and extremely strong expressivity, it is (ironically)  arguably the best way to learn Java. It has an outstanding community, a strong feature set, and some excellent code in the open-source world.

Lisp is also of strong fundamental importance, because its macro system is unlike anything else in any other language and will fundamentally alter how an engineer thinks about software, and because Lisp encourages people to use a very expressive style. It’s also an extremely productive language: large amounts of functionality can be delivered in a small amount of time. Lisp is a great language for learning the fundamentals of computing, and that’s one reason why Scheme has been traditionally used in education. (However, I’d probably advocate starting with Python because it’s easier to get to “real stuff” quickly in it. Structure and Interpretation of Computer Programs and Scheme should be presented when people know they’re actually interested in computing itself.)

When one’s writing large systems, Lisp isn’t the best choice, because interfaces matter at that point, and there’s a danger that people will play fast-and-loose with interfaces (passing nested maps and lists and expecting the other side to understand the encoding) in a way that can be toxic. Lisp is great if you trust the developers working on the project, but (sadly) I don’t think many companies remain in such a state as they grow to scale.

Also, static typing is a feature, not a drawback. Used correctly, static typing can make code more clear (by specifying interfaces) and more robust, in addition to the faster performance usually available in compiled, statically typed languages. ML and Haskell (which I didn’t list, but it’s a great language in its own right) can teach a person how to use static typing well.

So after Lisp, the 5th language to master is Scala. Why Scala, after learning all those others, and having more than enough tools to program in interesting ways? First of all, it has an incredible amount of depth in its type system, which attempts to unify the philosophies of ML and Java and (in my opinion) does a damn impressive job. The first half of Types and Programming Languages is, roughly speaking, the theoretic substrate for ML. But ML doesn’t have a lot of the finer features. It doesn’t have subtyping, for example. Also, the uniqueness constraint on record and discriminated union labels (necessary for full Hindley-Milner inference, but still painful) can have a negative effect on the way people write code. The second half of TAPL, which vanilla ML doesn’t really support, is realized in Scala. Second, I think Scala is the language that will salvage the 5 percent of object-oriented programming that is actually useful and interesting, while providing such powerful functional features that the remaining 95% can be sloughed away. The salvage project in which a generation of elite programmers selects what works from a variety of programming styles– functional, object-oriented, actor-driven, imperative– and discards what doesn’t work, is going to happen in Scala. So this is a great opportunity to see first-hand what works in language design and what doesn’t.

Scala’s a great language that also requires taste and care, because it’s so powerful. I don’t agree with the detractors who claim it’s at risk of turning into C++, but it definitely provides enough rope for a person to hang himself by the monads.

What’s most impressive about Clojure and Scala is their communities. An enormous amount of innovation, not only in libraries but also in language design, is coming out of these two languages. There is a slight danger of Java-culture creep in them, and Scala best practices (expected by the leading build environments) do, to my chagrin, involve directories called “src” and “main” and even seem to encourage singleton directories called “com”, but I’m willing to call this a superficial loss and, otherwise, the right side seems to be winning. There’s an incredible amount of innovation happening in these two languages that have now absorbed the bulk of the top Java developers.

Now… I mentioned “six languages” in this post’s title but named five. The sixth is one that very few programmers are willing to use in source code: English. (Or, I should say, the scientifically favored natural language of one’s locale.) Specifically, technical English, which requires rigor as well as clarity and taste. Written communication. This is more important than all of the others. By far. For that, I’m not complaining that software engineers are bad at writing. Competence is not a problem. Anyone smart enough to learn C++ or the finer points of Lisp is more than intelligent enough to communicate in a reasonable way. I’m not asking people to write prose that would make Faulkner cry; I’m asking them to explain the technical assets they’ve created at, at the least, the level of depth and rigor expected in a B+ undergraduate paper. The lack of writing in software isn’t an issue of capability, though, but of laziness.

Here’s one you hear sometimes: “The code is self-documenting.” Bullshit. It’s great when code can be self-documenting, making comments unnecessary, but it’s pretty damn rare to be solving a problem so simple that the code responsible for solving it is actually self-explanatory. Most problems are custom problems that require documentation of what is being solved, why, and how. People need to know, when they read code, what they’re looking at; otherwise, they’re going to waste a massive amount of time focusing on details that aren’t relevant. Documentation should not be made a crutch– you should also do the other important things like avoiding long functions and huge classes– but it is essential to write about what you’re doing. People need to stop thinking about software as machinery that “explains itself” and start thinking of it as writing a paper, with instructions for humans about what is happening alongside the code actually doing the work.

One of the biggest errors I encounter with regard to commenting is the tendency to comment minutiae while ignoring the big picture. There might be a 900-line program with a couple comments saying, “I’m doing this it’s O(n) instead of O(n^2)” or “TODO: remove hard-coded filename”, but nothing that actually explains why these 900 lines of code exist. Who does that help? These types of comments are useless to people who don’t understand what’s happening at all, which they generally won’t in the face of inadequate documentation. Code is much easier to read when one knows what one is looking at, and microcomments on tiny details that seemed important when the code was written are not helpful.

Comments are like static typing: under-regarded if not ill-appreciated because so few people use them properly, but very powerful (if used with taste) in making code and systems actually legible and reusable. Most real-world code, unfortunately, isn’t this way. My experience is that about 5 to 10 percent of code in a typical codebase is legible, and quite possibly only 1 percent is enjoyable to read (which good code truly is). The purpose of a comment should not be only to explain minutiae or justify weird-looking code. Comments should also ensure that people always know what they’re actually looking at.

The fallacy that leads to a lack of respect for documentation is that writing code is like building a car or some other well-understood mechanical system. Cars don’t come with a bunch of labels on all the pieces, because cars are fairly similar under the hood and a decent mechanic can figure out what is what. With software, it’s different. Software exists to solve a new problem; if it were solving an old problem, old software could be used. Thus, no two software solutions are going to be the same. In fact, programs tend to be radically different from one another. Software needs to be documented because every software project is inherently different, at least in some respects, from all the others.

There’s another problem, and it’s deep. The 1990s saw an effort, starting with Microsoft’s visual studio, to commoditize programmers. The vision was that, instead of programming being a province of highly-paid, elite specialists with a history of not working well with authority, software could be built by bolting together huge teams of mediocre, “commodity” developers, and directing them using traditional (i.e. pre-Cambrian) management techniques. This has begun to fail, but not before hijacking object-oriented programming, turning Java’s culture poisonous, and creating some of the most horrendous spaghetti code (MudballVisitorFactoryFactory) the world has ever seen. Incidentally, Microsoft is now doing a penance by having its elite research division investigate functional programming in a major way, the results being F# and a much-improved C#. Microsoft, on the whole, may be doomed to mediocrity, but they clearly have a research division that “gets it” in an impressive way. Still, that strikes me as too little, too late. The damage has be done, and the legacy of the commodity-developer apocalypse still sticks around.

The result of the commodity-programmer world is the write-only code culture that is the major flaw of siloized, large-program development. That, I think, is the fundamental problem with Java-the-culture, IDE-reliance, and the general lack of curiosity observed (and encouraged) among the bottom 80 percent of programmers. To improve as programmers, people need to read code and understand it, in order to get a sense of what good and bad code even are, but almost no one actually reads code anymore. IDEs take care of that. I’m not going to bash IDEs too hard, because they’re pretty much essential if you’re going to read a typical Java codebase, but IDE culture is, on the whole, a major fail that makes borderline-employable programmers out of people who never should have gotten in in the first place.

Another problem with IDE culture is that the environment becomes extremely high maintenance, between plugins that often don’t work well together, build system idiosyncracies that accumulate over time, and the various menu-navigation chores necessary to keep the environment sane (as opposed to command-line chores, which are easily automated). Yes, IDEs do the job: bad code becomes navigable, and commodity developers (who are terrified of the command line and would prefer not to know what “build systems” or “version control” even are) can crank out a few thousand lines of code per year. However, the high-maintenance environment requires a lot of setup work, and I think this is culturally poisonous. Why? For a contrast, in the command-line world, you solve your own problems. You figure out how to download software (at the command line using wget, not clicking a button) and install it. Maybe it takes a day to figure out how to set up your environment, but once you’ve suffered through this, you actually know a few things (and you usually learn cool things orthogonal to the problem you were originally trying to solve). When a task gets repetitive, you figure out how to automate it. You write a script. That’s great. People actually learn about the systems they’re using. On the other hand, in IDE-culture, you don’t solve your own problems because you can’t, because there it would take too long. In the big-program world, software too complex for people to solve their own problems is allowed to exist. Instead of figuring it out on your own, you flag someone down who understands the damn thing, or you take a screenshot of the indecipherable error box that popped up and send it to your support team. This is probably economically efficient from a corporate perspective, but it doesn’t help people become better programmers over time.

IDE culture also creates a class of programmers who don’t work with technology outside of the office– the archetypal “5:01 developers”– because they get the idea that writing code requires an IDE (worse yet, an IDE tuned exactly in line with the customs of their work environment). If you’re IDE-dependent, you can’t write code outside of a corporate environment, because when you go home, you don’t have a huge support team to set the damn thing up in a way that you’re used to and fix things when the 22 plugins and dependencies that you’ve installed interact badly.

There are a lot of things wrong with IDE culture, and I’ve only scratched the surface, but the enabling of write-only code creation is a major sticking point. I won’t pretend that bad code began with IDEs because that’s almost certainly not true. I will say that the software industry is in a vicious cycle, which the commodity-developer initiative exacerbated. Because most codebases are terrible, people don’t read them. Because “no one reads code anymore”, the bulk of engineers never get better, and continue to write bad code.

Software has gone through a few phases of what it means for code to actually be “turned in” as acceptable work. Phase 1 is when a company decides that it’s no longer acceptable to horde personal codebases (that might not even be backed up!) and mandates that people check their work into version control. Thankfully, almost all companies have reached that stage of development. Version control is no longer seen as “subversive” by typical corporate upper management. It’s now typical. The second is when a company mandates that code have unit tests before it can be relied upon, and that a coding project isn’t done until it has tests. Companies are reaching this conclusion. The third milestone for code-civilizational development, which very few companies have reached, is that the code isn’t done until you’ve taught users how to use it (and how to interact with it, i.e. instantiate the program and run it or send messages to it, in a read-eval-print-loop appropriate to the language). That teaching can be supplied at a higher level in wikis, codelabs, and courses… but it also needs to be included with the source code. Otherwise, it’s code out-of-context, which becomes illegible after a hundred thousand lines or so. Even if the code is otherwise good, out-of-context code without clear entry-points and big-picture documentation becomes incomprehensible around this point.

What do I not recommend? There’s no language that I’d say is categorically not worth learning, but I do not recommend becoming immersed in Java (except well enough to understand the innards of Clojure and Scala). The language is inexpressive, but the problem isn’t the language, and in fact I’d say that it’s unambiguously a good thing for an engineer to learn how the JVM works. It’s that Java-the-Culture (VisitorSelectionFactories, pointless premature abstraction, singleton directories called “com” that betray dripping contempt for the command line and the Unix philosophy, and build environments so borked that it’s impossible not to rely on an IDE) that is the problem; it’s so toxic that it reduces an engineer’s IQ by 2 points per month.

For each of these five programming languages, I’d say that a year of exposure is ideal and probably, for getting a generalist knowledge, enough– although it takes more than a year to actually master any of these. Use and improvement of written communication, on the other hand, deserves more. That’s a lifelong process, and far too important for a person not to start early on. Learning new programming languages and, through this, new ways of solving problems, is important; but the ability to communicate what problem one has solved is paramount.

Ambition and what it taught me: the 4-factor model.

Nine years ago, I came up with a card game called Ambition in which I attempted to remove card-luck from a trick-taking card game. This turned out to be a surprisingly difficult (and very fun) design problem. To give a 50,000-foot overview, the original game was one in which the goal was to get a middling score each round, making the objective more about manipulating the flow of the game (and the players) rather than trying to take (e.g. Bridge) or avoid (e.g. Hearts) tricks. The original game had only the middling objective, but as with Hearts and it’s “shooting the moon” reversal, I had to add high-risk, high-reward strategies for very strong (Slam) and very weak (Nil) hands. What I ended up building was a game where card-luck has a very small influence, because every hand has a credible strategy.

I’ve estimated that card-luck produces about 5% of the variation in a typical 2-hour game. (This could be reduced to 3-4% by reducing the Slam bonus, but that would also make the game less fun, so what would be the point?) For a trick-taking game, this is rare. Now, Bridge is an immensely skillful game, but it’s got a lot of card luck in the short term. For this reason, Bridge players duplicate the game in serious settings, which means that they play the same hands as everyone else in the club and are scored on their relative performance. A typical Bridge tournament might have 20 teams– or 40 people. I don’t think there are 40 Ambition players in a given state at any time, so duplication’s not an option.

Why’d I want to eliminate card luck from a trick-taking game? The short version of the story is that I had caught that German board game bug, but I was in Budapest for a semester (at this program) and had only a deck of cards. But I’d fallen in love with the German design aesthetic. Also, experience had led me to conclude that the games regarded as being the most interesting, and the ones that become culturally important, tend to be skillful games. Go, Chess, and Bridge are all very deep and skillful games, which makes their outcomes meaningful and indicative of genuine skill (or decisive). Poker becomes skillful with enough patience; viewed as one game played over a person’s life, it converges, as most games well. This led down the rabbit hole of “luck/skill balance”. What is it? Oddly enough, I concluded that it doesn’t exist, at least not when framed as a linear dichotomy.

The idea of “luck vs. skill” places Go (a very deep, skillful game) at one end of a continuum and Bingo (which is pure chance) at the other. As this ideology goes, luck games are cotton-candy junk food, and skill games are, if a bit dry, respectable and rewarding. Supporting this is that the culturally successful and long-lived “mind sports” tend to be highly skillful, which seems to imply that if you want to design a “good” game, you should be aiming to get rid of luck. The problem with the luck/skill dichotomy is that there are a number of game mechanics it fails to model. For a trivial example, Rock, Paper, Scissors contains no randomizer but (at one iteration) is effectively “random”, because it presents simultaneous decision-making with a perfectly-symmetrical strategic shape (i.e. no strategy is functionally different from any other). Rock, Paper, Scissors at one iteration can be considered to be effectively a luck game, so what about the iterated version. Is the long-term, iterated game luck-driven or skillful? That’s a surprisingly hard question to answer, even theoretically. For a more practical example, consider multi-player German-style favorites like Puerto Rico, an excellent game sometimes criticized for the influence of table position (i.e. the difference between sitting to the left vs. the right of the best player can have a measurable affect on one’s outcome). There are almost no random elements to this game, but play order becomes an influence. Is that aspect– knowing where to sit– luck or is it skill? (Answer: it’s meta-game.) But the biggest problem with the luck/skill dichotomy is that it breaks down completely when there are more than 2 players. In a 3-player game, an unpredictable, nonconventional, or outright incompetent player can make strategic choices that favor one player over the other– an effect deriving neither from a truly random element of the game (such as dice or a deck of cards) nor from those players’ individual levels of skill. This “interaction term” element is strategy: a mix of luck and skill inherent in simultaneous, poly-agent decision making.

The difference between a demonstration of skill and “strategic luck” is that the former will generally affect opponents’ outcomes in a non-biased way. If Alice does something that gives her an advantage over Bob and Eve both, she’s playing skillfully, not getting lucky. If she does something that unintentionally or chaotically gives Bob an advantage over Eve and Bob wins, that’s strategic luck favoring Bob.

In two-party games, there is no strategic luck. If the opponent’s strategy causes one to lose, that was (by definition) skillful play, not strategic interference. Likewise applying to two-team games (like Bridge) it is accurate to say that friendly “strategic luck” is skill.

However, in games of 3 or more players, it’s pretty much impossible to eliminate strategic luck (not that I’m convinced that it would be desirable to do so). This is reminiscent of Arrow’s Impossibility Theorem, which state that it’s impossible to design a “perfectly fair” voting system, where “fair” means that the presence or absence of a candidate C should not affect the relative performance of A and B (i.e. no “Nader effect”.) Games with three- or more players face an inherent trade-off between (a) restricting interactions between players, making the game less fun, versus (b) encouraging them but introducing strategic luck. So with large groups, it’s often better for a game designer to just own the strategic luck and make the alliance-forming (and -breaking) aspects a core element, as with Diplomacy or Apples to Apples.

This may be why the games that develop the mind sport culture always seem to be 2-party games. A game of 3 or more players without strategic luck would have to be structured too much like “multiplayer solitaire” to be fun, but one with strategic luck is unlikely to develop a tournament scene, as the cultural purpose of those is to determine “the best” player. (When there’s strategic luck, the best player can be undefined. Alice may be superior to Bob when Carla sits at the table, while Bob is better than Alice when Dan is playing.)

As for Ambition, I removed the card luck but I introduced some strategic luck. A “bad” hand can lead to a great outcome based on unrelated prior decisions by other players. Strategic luck is noticable. Which made it not quite like Go or Chess where a superior player can expect to win 95+ percent of the time, but more like a German-style game where pure chance factors are uncommon (you rarely feel “screwed” by the cards) but strategic luck is tolerated. And that’s fine. It adds to the fun, and it’s a casual game, after all.

Luck, skill, and strategy are 3 factors that determine players’ outcomes in a game. Pure chance elements can be isolated and assessed mathematically. Skill an usually be quantified, by observing players’ outcomes and adjusting, as with the ELO system. As for strategy? It’s completely impossible to quantify this element in a general way, because the strategic variables within a game are, in some sense, the spinal shape of the game itself. Pure chance elements can be analyzed through statistical means, but there’s no general-purpose way to measure strategic luck. I’m not sure if I can even precisely define it.

I said there would be 4 factors, so what’s the fourth? The most interesting one, which I call flux. To explain flux, consider one important observation pertaining to supposedly “purely skillful” games: they don’t have the same outcome every time they’re played. If they did, they’d actually be frustrating and boring, even for nearly exactly matched players. Thankfully, that’s not the case. Alice defeating Bob does not mean that Alice will always beat Bob. What this means is that there’s something subtle– an energy– that makes the game a real contest when it’s played between players who are close in skill level.

Flux is minute-to-minute variation in a player’s skill and strategic effectiveness. Positive flux is being “in flow”– the state of consciousness that makes games (and programming, and writing, and many other things) fun. It’s a state of mind in which a person has above-normal concentration, confidence, ability to assess risk, and effectiveness in execution. Negative flux is the opposite, and it’s described by poker players as being “on tilt”. It’s being out of flow. When players of equal or near-equal skill compete, it’s often flux that determines the winner. And that’s what makes such contests exciting– the fact that the game is skillful and decisive (so the outcome actually matters) but that, because the contestants are close in skill level, the end-of-game binary outcome (winning vs. losing) is going to be determined by minute-to-minute fluctuations in animal energies. Flow. Flux. “The zone.”

Luck, skill and strategy are all important tools in a game designer’s arsenal as he pursues his design goal (which is not to land at a targeted point on some bullshit “luck/skill continuum”, but to design a game that’s fun to play). Luck gives more players a chance at . Skillful elements make the game decisive and more . Strategy, on the other hand, is what makes multiplayer games interactive and social. All of these elements can be quite effective at making a game fun. But it’s the tense real-time drama of flux as players go into and drop out of flow that really makes a game interesting.

Don’t waste your time in crappy startup jobs.

What I’m about to say is true now, as of July 2012. It wasn’t necessarily true 15 years ago, and it may not be true next year. Right now, for most people, it’s utterly correct– enough that I feel compelled to say it. The current VC-funded startup scene, which I’ve affectionately started calling “VC-istan”, is– not to be soft with it– a total waste of time for most of the people involved.

Startups. For all the glamour and “sexiness” associated with the concept, the truth is that startups are no more and no less than what they sound like: new, growing businesses. There are a variety of good and bad reasons to join or start businesses, but for most of human history, it wasn’t viewed as a “sexy” process. Getting incorporated, setting up a payroll system, and hiring accountants are just not inspiring duties for most people. They’re mundane tasks that people are more than willing to do in pursuit of an important goal, but starting a business has not typically been considered to be  inherently “sexy”. What changed, after about 1996, is that people started seeing ”startups” as an end in themselves. Rather than an awkward growth phase for an emerging, risky business, “startup” became a lifestyle. This was all fine because, for decades, positions at established businesses were systemically overvalued by young talent, and those at growing small companies were undervalued. It made economic sense for ambitious young people to brave the risk of a startup company. Thus, the savviest talent gravitated toward the startups, where they had access to responsibilities and career options that they’d have to wait for years to get in a more traditional setting.

Now, the reverse seems to be true. In 1995, a lot of talented young people went into large corporations because they saw no other option in the private sector– when, in fact, there were credible alternatives, startups being a great option. In 2012, a lot of young talent is going into startups for the same reason: a belief that it’s the only legitimate work opportunity for top talent, and that their careers are likely to stagnate if they work in more established businesses. They’re wrong, I think, and this mistaken belief allows them to be taken advantage of. The typical equity offer for a software engineer is dismally short of what he’s giving up in terms of reduced salary, and the career path offered by startups is not always what it’s made out to be.

For all this, I don’t intend to argue that people shouldn’t join startups. If the offer’s good, and the job looks interesting, it’s worth trying out. I just don’t think that the current, unconditional “startups are awesome!” mentality serves us well. It’s not good for any of us, because there’s no tyrant worse than a peer selling himself short, and right now there are a lot of great people selling themselves very short for a shot at the “startup experience”– whatever that is.

Here are 7 misconceptions about startups that I’d like to dispel.

1. A startup will make you rich. True, for founders, whose equity shares are measured in points. Not true for most employees, who are offered dimes or pennies.

Most equity offerings for engineers are, quite frankly, tiny. A “nickel” (0.05 percent) of an 80-person business is nothing to write home about. It’s not partnership or ownership. Most engineers have the mistaken belief that the initial offering is only a teaser, and that it will be improved once they “prove themselves”, but it’s pretty rare that this actually happens.

Moreover, raises and bonuses are very uncommon in startups. It’s typical for high performers to be making the same salary after 3 years as they earned when they started. (What happens to low performers, and to high performers who fail politically? They get fired, often with no warning or severance.) Substantial equity improvements are even rarer. When things are going well in a startup, the valuation of the equity package is increasing and that is the raise. When things are going badly, that’s the wrong time to be asking for anything.

There are exceptions. One is that, if the company finds itself in very tough straits and can’t afford to pay salaries at all, it will usually grant more equity to employees in order to make up for the direct economic hardship it’s causing them by not being able to pay a salary. This isn’t a good situation, because the equity is usually offered at-valuation (more specifically, at the valuation of the last funding round, when the company was probably in better shape) and typically employees would be better off with the cash. Another is that it’s not atypical for a company to “refresh” or lengthen a vesting period with a proportionate increase. A 0.1% grant, vesting over four years, can be viewed as compensation at 0.025% per year. It’s not atypical for a company to continue that same rate in the years after that. That means that a person spending six years might get up to 0.15%. What is atypical is for an employee brought in with 0.1% to be raised to 1% because of good performance. The only time that happens is when there’s a promotion involved, and internal promotions (more on this, later) are surprisingly rare in startups.

2. The “actual” valuation is several times the official one. This is a common line, repeated both by companies in recruiting and by engineers justifying their decision to work for a startup. (“My total comp. is actually $250,000 because the startup really should be worth $5 billion.) People love to think they’re smarter than markets. Usually, they aren’t. Moreover, the few who are capable of being smarter than markets are not taking (or trying to convince others to take) junior-level positions where the equity allotment is 0.05% of an unproven business. People who’ve legitimately developed that skill (of reliably outguessing markets) deal at a much higher level than that.

So, when someone says, “the actual valuation should be… “, it’s reasonable to conclude with high probability that this person doesn’t know what the fuck he or she is talking about.

In fact, an engineer’s individual valuation should, by rights, be substantially lower than the valuation at which the round of funding is made. When a VC offers $10 million for 20% of a business, the firm is stating that it believes the company (pre-money) is worth $40 million to them. Now, startup equity is always worth strictly more (and by a substantial amount) to a VC than it is worth to an engineer. So the fair economic value (for an engineer) of a 0.1% slice is probably not $40,000. It might be $10-20,000.

There are several reasons for this disparity of value. First, the VC’s stake gives them control. It gives them board seats, influence over senior management, and the opportunity to hand out a few executive positions to their children or to people whom they owe favors. An engineer’s 0.1% slice, vesting over four years, doesn’t give him any control, respect, or prestige. It’s a lottery ticket, not a vote. Second, startup equity is a high-risk asset, and VCs have a different risk profile from average people. An average person would rather have a guarantee of $2 million than a 50% chance of earning $5 million, even though the expected value of the latter offer is higher. VCs, in general, wouldn’t, because they’re diversified enough to take the higher-expectancy, riskier choices. Third, the engineer has no protection against dilution, and will be on the losing side of any preference structure that the investors have set up (and startups rarely volunteer information pertaining to what preferences exist against common stock, which is what the engineers will have). Fourth, venture capitalists who invest in highly successful businesses get prestige and huge returns on investment, whereas mere employees might get a moderate-sized windfall, but little prestige unless they achieved an executive position. Otherwise, they just worked there.

In truth, startup employees should value equity and options at about one-fourth the valuation that VCs will give it. If they’re giving up $25,000 per year in salary, they should only do so in exchange for $100,000 per year (at current valuation) in equity. Out of a $40-million company with a four-year vesting cycle, that means they should ask for 1%.

3. If you join a startup early, you’re a shoe-in for executive positions. Nope.

Points #1-2 aren’t going to surprise many people. Most software engineers know enough math to know that they won’t get filthy rich on their equity grants, but join startups under the belief that coming into the company early will guarantee a VP-level position at the company (at which point compensation will improve) once it’s big. Not so. In fact, one of the best ways not to get a leadership position in a startup is to be there early.

Startups often involve, for engineers, very long hours, rapidly changing requirements, and tight deadlines, which means the quality of the code they write is generally very poor in comparison to what they’d be able to produce in saner conditions. It’s not that they’re bad at their jobs, but that it’s almost impossible to produce quality software under those kinds of deadlines. So code rots quickly in a typical startup environment, especially if requirements and deadlines are being set by a non-technical manager. Three years and 50 employees later, what they’ve built is now a horrific, ad-hoc, legacy system hacked by at least ten people and built under intense deadline pressure, and even the original architects don’t understand it. It may have been a heroic effort to build such a powerful system in so little time, but from an outside perspective, it becomes an embarrassment. It doesn’t make the case for a high-level position.

Those engineers should, by rights, get credit and respect for having built the system in the first place. For all its flaws, if the system works, then the company owes no small part of its success to them. Sadly, though, the “What have you done for me lately?” impulse is strong, and these engineers are typically associated with how their namesake projects end (as deadline-built legacy monstrosities) rather than what it took to produce them.

Moreover, the truth about most VC-funded startups is that they aren’t technically deep, so it seems to most people that it’s marketing rather than technical strength that determines which companies get off the ground and which don’t. The result of this is that the engineer’s job isn’t to build great infrastructure that will last 10 years… because if the company fails on the marketing front, there will be no “in 10 years”. The engineer’s job is to crank out features quickly, and keep the house of cards from falling down long enough to make the next milestone. If this means that he loads up on “technical debt”, that’s what he does.

If the company succeeds, it’s the marketers, executives, and biz-dev people who get most of the glory. The engineers? Well, they did their jobs, but they built that disliked legacy system that “just barely works” and “can’t scale”. Once the company is rich and the social-climbing mentality (of always wanting “better” people) sets in, the programmers will be replaced with more experienced engineers brought in to “scale our infrastructure”. Those new hires will do a better job, not because they’re superior, but because the requirements are better defined and they aren’t working under tight deadline pressure. When they take what the old-timers did and do it properly, with the benefit of learning from history, it looks like they’re simply superior, and managerial blessing shifts to “the new crowd”. The old engineers probably won’t be fired, but they’ll be sidelined, and more and more people will be hired above them.

Furthermore, startups are always short on cash and they rarely have the money to pay for the people they really want, so when they’re negotiating with these people in trying to hire them, they usually offer leadership roles instead. When they go into the scaling phase, they’re typically offering $100,000 to $150,000 per year for an engineer– but trying to hire people who would earn $150,000 to $200,000 at Google or on Wall Street. In order to make their deals palatable, they offer leadership roles, important titles and “freedom from legacy” (which means the political pull to scorched-earth existing infrastructure if they dislike it or it gets in their way) to make up for the difference. If new hires are being offered leadership positions, this leaves few for the old-timers. The end result of this is that the leadership positions that early engineers expect to receive are actually going to be offered away to future hires.

Frankly put, being a J.A.P. (“Just A Programmer”) in a startup is usually a shitty deal. Unless the company makes unusual cultural efforts to respect engineering talent (as Google and Facebook have) it will devolve into the sort of place where people doing hard things (i.e. software engineers) get the blame and the people who are good at marketing themselves advance.

4. In startups, there’s no boss. This one’s patently absurd, but often repeated. Those who champion startups often say that one who goes and “works for a company” ends up slaving away for “a boss” or “working for The Man”, whereas startups are a path to autonomy and financial freedom.

The truth is that almost everyone has a boss, even in startups. CEOs have the board, the VPs and C*Os have the CEO, and the rest have actual, you know, managers. That’s not always a bad thing. A competent manager can do a lot for a person’s career that he wouldn’t realistically be able to do on his own. Still, the idea that joining a startup means not having a boss is just nonsense.

Actually, I think founders often have the worst kind of “boss” in venture capitalists. To explain this, it’s important to note that the U.S. actually has a fairly low “power distance” in professional workplaces– this is not true in all cultures– by which I mean bosses aren’t typically treated as intrinsic social superiors to their direct reports. Yes, they have more power and higher salaries, but they’re also older and typically have been there for longer. A boss who openly treats his reports with contempt, as if he were innately superior, isn’t going to last for very long. Also, difficult bosses can be escaped: take another job. And the most adverse thing they can (legally) do is fire someone, which has the same effect. Beyond that, bosses can’t legally have a long-term negative effect on someone’s career.

With VCs, the power distance is much greater and the sense of social superiority is much stronger. For example, when a company receives funding it is expected to pay both parties’ legal fees. This is only a minor expenditure in most cases, but it exists to send a strong social message: you’re not our kind, dear, and this is what you’ll deal with in order to have the privilege of speaking with us at all. 

This is made worse by the incestuous nature of venture capital, which leads to the worst case of groupthink ever observed in a supposedly progressive, intelligent community. VCs like a startup if other VCs like it. The most well-regarded VCs all know each other, they all talk to each other, and rather than competing for the best deals, they collude. This leaves the venture capitalists holding all the cards. A person who turns down a term sheet with multiple liquidation preferences and participating preferred (disgusting terms that I won’t get into because they border on violence, and I’d prefer this post to be work-safe) is unlikely to get another one.

A manager who presents a prospective employee with a lowball offer and says, “If you don’t take this, I’ll make a phone call and no one in the industry will hire you” is breaking the law. That’s extortion. In venture capital? They don’t have to say this. It’s unspoken that if you turn down a terrible term sheet with a 5x liquidation preference, you’re taking a serious risk that a phone call will be made and that supposedly unrelated interest will dry up as well. That’s why VCs can get away with multiple liquidation preferences and participating preferred.

People who really don’t want to have “a boss” should not be looking into VC-funded startups. There are great, ethical venture capitalists who wouldn’t go within a million miles of the extortive shenanigans I’ve described above. It’s probably true that most are. Even still, the power relationship between a founder and investor is far more lopsided than that between a typical employee and manager. No manager can legally disrupt an employee’s career outside of one firm; but venture capitalists can (and sometimes do) block people from being fundable.

Instead, those who really want not to have a boss should be thinking about smaller “lifestyle” businesses in which they’ll maintain a controlling interest. VC has absolutely no interest in funding these sorts of companies, so this is going to require angel investment or personal savings, but for those who really want that autonomy, I think this is the best way to go.

For all this, what I’ve said here about the relationship between founders and VCs isn’t applicable to typical engineers. An engineer joining a startup of larger than about 20 people will have a manager, in practice if not in reality. That’s not a bad thing. It’s no worse or better than it would be in any other company. It does make the “no boss” vs. “working for The Man” selling point of startups a bit absurd, though.

5. Engineers at startups will be “changing the world”. With some exceptions, startups are generally not vehicles for world-changing visions. Startups need to think about earning revenue within the existing world, not “changing humanity as we know it”.

“The vision thing” is an aspect of the pitch that is used to convince 22-year-old engineers to work for 65 percent of what they’d earn at a more established company, plus some laughable token equity offering. It’s not real.

The problem with changing the world is that the world doesn’t really want to change, and to the extent that it it’s willing to do so, few people who have the resources necessary to push for improvements. What fundamental change does occur is usually gradual– not revolutionary– and requires too much cooperation to be forced through by a single agent.

Scientific research changes the world. Large-scale infrastructure projects change the world. Most businesses, on the other hand, are incremental projects, and there’s nothing wrong with that. Startups are not a good vehicle for “changing the world”. What they are excellent at is finding ways to profit from inexorable, pre-existing trends by doing things that (a) have recently become possible, but that (b) no one had thought of doing (or been able to do) before. By doing so, they often improve the world incrementally: they wouldn’t survive if they didn’t provide value to someone. In other words, most of them are application-level concepts that fill out an existing world-changing trend (like the Internet) but not primary drivers. That’s fine, but people should understand that their chances of individually effecting global change, even at a startup, are very small.

6. If you work at a startup, you can be a founder next time around. What I’ve said so far is that it’s usually a shitty deal to be an employee at a startup: you’re taking high risk and low compensation for a job that (probably) won’t make you rich, lead to an executive position, bring great autonomy, or change the world. So what about being a founder? It’s a much better deal. Founders can get rich, and they will make important connections that will set up their careers. So why aren’t more people becoming founders of VC-funded startups? Well, they can’t. Venture capital acceptance rates are well below 1 percent.

The deferred dream is probably the oldest pitch in the book, so this one deserves address. A common pitch delivered to prospective employees in VC-istan is that “this position will set you up to be a founder (or executive) at your next startup”. Frankly, that’s just not true. The only thing that a job can offer that will set a person up with the access necessary to be a founder in the future is investor contact, and a software engineer who insists on investor contact when joining an already-funded startup is going to be laughed out the door as a “prima donna”.

A non-executive position without investor contact at a startup provides no more of the access that a founder will need than any other office job. People who really want to become startup founders are better off working in finance (with an aim at venture capital) or pursuing MBA programs than taking subordinate positions at startups.

7. You’ll learn more in a startup. This last one can be true; I disagree with the contention that it’s always true. Companies tend to regress to the mean as they get bigger, so the outliers on both sides are startups. And there are things that can be learned in the best small companies when they are small that can’t be learned anywhere else. In other words, there are learning opportunities that are very hard to come by outside of a startup.

What’s wrong here is the idea that startup jobs inherently more educational simply because they exist at startups. There’s genuinely interesting work going on at startups, but there’s also a hell of a lot of grunt work, just like anywhere else. On the whole, I think startups invest less in career development than more established companies. Established companies have had great people leave after 5 years, so they’ve had more than enough time to “get it” on the matter of their best people wanting more challenges. Startups are generally too busy fighting fires, marketing themselves, and expanding to have time to worry about whether their employees are learning.

So… where to go from here?

I am not trying to impart the message that people should not work for startups. Some startups are great companies. Some pay well and offer career advancement opportunities that are unparalleled. Some have really great ideas and, if they can execute, actually will make early employees rich or change the world. People should take jobs at startups, if they’re getting good deals.

Experience has led me to conclude that there isn’t much of a difference in mean quality between large and small companies, but there is a lot more variation in the small ones, for rather obvious reasons. The best and worst companies tend to be startups. The worst ones don’t usually live long enough to become big companies, so there’s a survivorship bias that leads us to think of startups as innately superior. It’s not the case.

As I said, the worst tyrant in a marketplace is a peer selling himself short. Those who take terrible deals aren’t just doing themselves a disservice to themselves, but to all the rest of us as well. The reason young engineers are being offered subordinate J.A.P. jobs with 0.03% equity and poorly-defined career tracks is because there are others who are unwise enough to take them.

In 2012, is there “a bubble” in internet startups? Yes and no. In terms of valuations, I don’t think there’s a bubble. Or, at least, it’s not obvious to me that one exists. I think it’s rare that a person who’s relatively uninformed (such as myself, when it comes to pricing technology companies) can outguess a market, and I see no evidence that the valuations assigned to these companies are unreasonable. Where there is undeniably a bubble is in the extremely high value that young talent is ascribing to subordinate positions at mediocre startups.

So what is a fair deal, and how does a person get one? I’ll give some very basic guidelines.

1. If you’re taking substantial financial risk to work at the company, you’re a Founder. Expect to be treated like one. By “substantial financial risk”, I mean earning less  than (a) the baseline cost-of-living in one’s area or (b) 75% of one’s market compensation.

If you’re taking that kind of risk, you’re an investor and you better be seen as a partner. It means you should demand the autonomy and respect given to a founder. It means not to take the job unless there’s investor contact. It means you have a right to know the entire capitalization structure (an inappropriate question for an employee, but a reasonable one for a founder) and determine if it’s fair, in the context of a four-year vesting period. (If the first technical hire gets 1% for doing all the work and the CEO gets 99% because he has the connections, that’s not fair. If the first technical hire gets 1% while the CEO gets 5% and the other 94% has been set aside for employees and investors, and the CEO has been going without salary for a year already, well, that’s much more fair.) It means you should have the right to represent yourself to the public as a Founder.

2. If you have at least 5 years of programming experience and the company isn’t thoroughly “de-risked”, get a VP-level title. An early technical hire is going to be spending most of his time programming– not managing or sitting in meetings or talking with the press as an “executive” would. Most of us (myself included) would consider that arrangement, of getting to program full-time at high productivity, quite desirable. This might make it seem like “official” job titles (except for CEO) don’t matter and that they aren’t worth negotiating for. Wrong.

Titles don’t mean much when 4 people at the company. Not in the least. So get that VP-level title locked-in now, before it’s valuable and much harder to get. Once there are more than about 25 people, titles start to have real value and for a programmer to ask for a VP title might seem like an unreasonable demand.

People may claim that titles are old-fashioned and useless and elitist, and they often have strong points behind their claims. Still, people in organizations place a high value on institutional consistency (meaning that there’s additional cognitive load for them to contradict the company’s “official” statements, through titles, about the status of its people) and the high status, however superficial and meaningless, conferred by an impressive title can easily become self-perpetuating. As the company becomes larger and more opaque, the benefit conferred by the title increases.

Another benefit of having a VP-level title is the implicit value inherent of being VP of something. It means that one will be interpreted as representing some critical component of the company. It also makes it embarrassing to the top executives and the company if this person isn’t well treated. For an example, let’s take “VP of Culture”. Doesn’t it sound like a total bullshit title? In a small company, it probably is. So meaningless, in fact, that most CEOs would be happy to give it away. “You want to be ‘VP of Culture’, but you’ll be doing the same work for the same salary? By all means.”  Yet what does it mean if a CEO berates the VP of Culture? That culture isn’t very important at this company. What about if the VP of Culture is pressured to resign or fired? From a public view, the company just “lost” its VP of Culture. That’s far more indicative than if a “J.A.P.” engineer leaves.

More relevantly, a VP title puts an implicit limit on the number of people who can be hired above a person, because most companies don’t want the image of having 50 of their 70 people being “VP” or “SVP”. It dilutes the title, and makes the company look bloated (except in finance, where “VP” is understood to represent a middling and usually not executive level.) If you’re J.A.P., the company is free to hire scads of people above you. If you’re a VP, anyone hired above you has to be at least a VP, if not an SVP, and companies tend to be conservative with those titles once they start to actually matter.

The short story to this is that, yes, titles are important and you should get one if the company’s young and not yet de-risked. People will say that titles don’t mean anything, and that “leadership is action, not position”, and there’s some truth in that, but you want the title nonetheless. Get it early when it doesn’t matter, because someday it will. And if you’re a competent mid-career (5+ years) software engineer and the company’s still establishing itself, then having some VP-level title is a perfectly reasonable term to negotiate.

3. Value your equity or options at one-fourth of the at-valuation level.  This has been discussed above. Because this very risky asset is worth much more to diversified, rich investors than it is to an employee, it should be discounted by a factor of 3-4. This means that it’s only worth it to take a job at $25,000 below market in exchange for $100,000 per year in equity or options (at valuation).

Also worth keeping in mind is that raises and bonuses are uncommon in startups, and that working at a startup can have an affect on one’s salary trajectory. Realistically, a person should assess a startup offer in the light of what he expects to earn over the next 3 to 5 years, not what he can command now.

4. If there’s deferred cash involved, get terms nailed down. This one doesn’t apply to most startups, because it’s an uncommon arrangement after a company is funded for it to be paying deferred cash. Usually, established startups pay a mix of salary and equity.

If deferred cash is involved in the package, it’s important to get a precise agreement on when this payment becomes due. Deferred cash is, in truth, zero-interest debt of the company to the employee. Left to its own devices, no rationally acting company would ever repay a zero-interest loan. So this is important to get figured out. What events make deferred cash due? (Startups never have “enough” money, so “when we have enough” is not valid.) What percentage of a VC-funding round is dedicated to pay off this debt? What about customer revenue? It’s important to get a real contract to figure this out; otherwise, the deferred payment is just a promise, and sadly those aren’t always worth much.

The most important matter to address when it comes to deferred cash is termination, because being owed money by a company one has left (or been fired from) is a mess. No one ever expects to be fired, but good people get fired all the time. In fact, there’s more risk of this in a small company, where transfers tend to be impossible on account of the firm’s small size, and where politics and personality cults can be a lot more unruly than they are in established companies.

Moreover, severance payments are extremely uncommon in startups. Startups don’t fear termination lawsuits, because those take years and startups assume they will either be (a) dead, or (b) very rich by the time any such suit would end– and either way, it doesn’t much matter to them. Being fired in established companies usually involves a notice (“improvement plan”) period (in which anyone intelligent will line up another job) or severance, or both, because established companies really don’t want to deal with termination lawsuits. In startups, people who are fired usually get neither notice nor severance.

People tend to think that the risk of startups is limited to the threat of them going out of business, but the truth is that they also tend to fire a lot more people, and often with less justification for doing so. This isn’t always a bad thing (firing too few people can be just as corrosive as firing too many) but it is a risk people need to be aware of.

I wouldn’t suggest asking for a contractual severance arrangement in negotiation with a startup; that request will almost certainly be denied (and might be taken as cause to rescind the offer). However, if there’s deferred cash involved, I would ask for a contractual agreement, if there is deferred cash, that it becomes due immediately on event of involuntary termination. Day-of, full amount, with the last paycheck.

5. Until the company’s well established (e.g. IPO) don’t accept a “cliff” without a deferred-cash arrangement in event of involuntary termination. The “cliff” is a standard arrangement in VC-funded startups whereby no vesting occurs if the employee leaves or is fired in the first year. The problem with the cliff is that it creates a perverse incentive for the company to fire people before they can collect any equity.

Address the cliff as follows. If employee is involuntarily terminated, and the cliff is enforced, whatever equity would have vested is converted (at most recent valuation) to cash and due upon date of termination.

This is a non-conventional term, and many startups will flat-out refuse it. Fine. Don’t work for them. This is important; the last thing you want is for the company to have an incentive to fire you because of a badly-structured compensation package.

6. Keep moving your career forward. Just being “at a startup” is not enough. The most credible appeal of working at a startup is the opportunity to learn a lot, and one can, it’s not a guarantee. Startups tend to be more “self-serve” in terms of career development. People who go out of their way to explore and use new technologies and approaches to problems will learn a lot. People who let themselves get stuck with the bulk of the junior-level grunt work won’t.

I think it’s useful to explicitly negotiate project allocation after the first year– once the “cliff” period is over. Raises being rare at startups, the gap between an employee’s market value and actual compensation is only growing as time goes by. When the request for a raise is denied is a good time to bring up the fact that you really would like to be working on that neat machine learning project or that you’re really interested in trying out a new approach to a problem the company faces.

7. If blocked on the above, then leave. The above are reasonable demands, but they’re going to meet some refusal because there’s no shortage of young talent that is right now willing to take very unreasonable terms for the chance to work “at a startup”. So expect some percentage of these negotiations to end in denial, even to the point of rescinded job offers. For example, some startup CEOs will balk at the idea that a “mere” programmer, even if he’s the first technical hire, wants investor contact. Well, that’s a sign that he sees you as “J.A.P.” Run, don’t walk, away from him.

People tend to find negotiation to be unpleasant or even dishonorable, but everyone in business negotiates. It’s important. Negotiations are indicative, because in business politeness means little, and so only when you are negotiating with someone do you have a firm sense of how he really sees you. The CEO may pay you a million compliments and make a thousand promises about your bright future in the company, but if he’s not willing to negotiate a good deal, then he really doesn’t see you as amounting to much. So leave, instead of spending a year or two in a go-nowhere startup job.

In the light of this post’s alarmingly high word count, I think I’ll call it here. If the number of special cases and exceptions indicates a lack of a clear message, it’s because there are some startup jobs worth taking, and the last thing I want to do is categorically state that they’re all a waste of time. Don’t get me wrong, because I think most of VC-istan (especially in the so-called “social media” space) is a pointless waste of talent and energy, but there are gems out there waiting to be discovered. Probably. And if no one worked at startups, no one could found startups and there’d be no new companies, and that would suck for everyone. I guess the real message is: take good offers and work good jobs (which seems obvious to the point of uselessness) and the difficulty (as observed in the obscene length of this post) is in determining what’s “good”. That is what I, with my experience and observations, have attempted to do.