It should do more so you can do less

Originally published at Hacker Noon

Apple just released iOS 10.3, which for the first time puts one of their production platforms on their new filesystem, APFS. I’m pretty happy that they are finally replacing such an aging, obsolete piece of core technology, but I’m nowhere near as excited about it as John Siracusa is. All APFS seems to do is finally meet the basic needs of a modern operating system, and not even all of those. It doesn’t come anywhere near doing what I care about most in the filesystem, which is owning metadata so applications don’t.

As anyone following the discussion of federal surveillance knows, metadata has become at least as important to our lives as our data. It’s not just the phone calls, it’s who you called, when, and for how long. It’s not just the phone, it’s which ones are your favorites and how they relate to each other. This metadata is as important to us personally as it is to the NSA, because it’s what brings simplicity, usefulness, and most importantly accessibility to the reams of information we create and rely on.

The reason I’m disappointed in APFS is that it missed the opportunity to promote metadata into something as important as the data, and as a result I’ve largely lost hope that computers will ever be as useful and open as they can be.

I’ve been trying for months to convert from Apple’s Photos to Adobe’s Lightroom for photo management, and the biggest barriers to doing so are metadata. It’s easy to get all of the photos - just drag the Masters folder into Lightroom. Oh, you organized your photos into albums? Yeah, that’s all gone. You had thousands of photos categorized as favorites so you could easily find the best? Oops, we ignore that.

Google Photos has the exact same problem. I can’t imagine how anyone who cares even a snit about photos could migrate to it, because there’s literally no way to do so that preserves any metadata at all. You get to keep the photos, but not how you organized them, and your only option is to rely on what they think matters to you.

Can you imagine switching email providers but losing all of the information about who the email is from or when it was sent? That’s as idiotic as what’s happening here.

If my filesystem owned this metadata, on the other hand, no conversion would be necessary. Any application could be written to read and write it directly, or I could trivially use any scripting language I wanted to do so. In fact, I could easily switch back and forth between different apps for different purposes, because they would not be able to lock me into their proprietary databases.

This shift is hard to understand, as we’re so used to thinking of applications and their metadata as somehow intertwined. For those in the programming world, there’s a clean analogy that helps to explain the horror of the modern filesystem:

For most of the history of software, applications have been written to directly access databases. They needed to understand the structure of tables and rows, and they had to write and manage their own queries for interacting with that data. Because of the complexity of duplicating this ability, applications tended to own their data entirely, and no other apps could get access.

We’ve recently instead seen a trend towards building very simple data services on top of databases, so that no app directly accesses them. This data service provides an API that understands how all of the data works, and also provides all of the key operations that can be performed on the data. This way, any application can access the data - for both reading and writing - without worrying that its use of the data is somehow incompatible with another consumer. You get the added benefit that any optimizations in the data service help every consumer, not just the one or two apps you remember to change.

Those data services are exactly what have enabled modern applications to scale to the level they do. The application developer can focus on their goals for the data, relying on the data service to provide consistency, portable operations, and everything else.

In the land of the operating system, though, we have no such luck. Everyone who talks to the filesystem gets about the thinnest interface imaginable: Get me a blob of data. Yes, there’s basic metadata (who owns it, when it was created, etc.), but none of it is or can be custom to the application, which makes it useless. Can you imagine how unpleasant it would be if we had to shoehorn both the subject of an email and the subject of a photo into the same keyword?

As a result, everyone turns that blob that the filesystem is trusted with into a database, and that’s where they put all of the metadata. Some even put the data there, even if it means huge database files and entirely non-portable systems. Really great app developers then go to the effort of building a full read and write API on top of this database that allows you to manage and port everything in the database. That’s enough work that most people skip it, though.

I would never have seen this missed opportunity in the filesystem if I hadn’t used BeOS. I went to Reed College, where Steve Jobs famously attended briefly (I made the unfortunate mistake of graduating, unlike him). This meant I was a Mac user all through school, which were the bad days of Quadras and MacOS 8 and 9. shudder I switched to BeOS because, well, I could, and it’s what really turned me into an OS junkie. There were many awesome things about it (many of which are now in OS X), but my favorite became how much responsibility the OS took for metadata. Applications became smaller, simpler, and in many cases, you realized your whole concept of an application was a way of organizing metadata, which meant you could rely on the OS entirely, and needed to write no code.

The greatest irony is that the author of Be’s awesome filesystem, Dominic Giampaolo, is now a lead engineer on APFS. Through the work of him and the rest of the team at Be, I came away with a clear idea of the value of my metadata, and especially the benefit of the operating system putting it on center stage.

So while I’m happy that my phone is no longer running the oldest, crappiest filesystem around, and I’m looking forward to the days when my computers aren’t either, I’m not excited. Until it was released, I could always dream that Apple saw the same future I did.

Now that the new filesystem is here, that hope is lost.

Just because it’s working doesn’t make it right

Startups are in a constant life and death struggle, where a short succession of failures can destroy them completely, or a sudden string of successes can lead to growth and stardom. Exactly because of this constant risk of death, startups grasp at every flash of success, and their eventual failure can often be traced back to those successes they bet on, rather the roadblocks they hit.

This means what you decide to double down on is as important as the fires you decide to fight. This is true in any company - a specific form of this is covered in depth by Clayton Christensen in “Innovator’s Dilemma”, where successful companies over-solve their customers’ problems rather than finding additional customers. It’s especially true for startups, though, which are so risky that every potential customer, employee, investor, or partner can be the difference between making payroll and going bankrupt.

Steve Blank and Eric Reis have done a great job of teaching the world about the importance of quick learning and iterating until you get wins, but reality is more complicated. Say you’re in the middle of rapid product iteration and you get a rash of customers who sign up but are completely different from those you have been trying to attract. Is this awesome or horrifying?

How could new customers be such bad news, you ask? Easy: They could be the wrong customers. “Wrong” could mean many things. They could be from a very small market, such that success with them isn’t enough to build a scalable business; they could be unprofitable, in that they’ll be very high-cost to acquire and support, so every added customer loses you money over time rather than making it; and most commonly, they’re just not the customer your company strategy set out to find.

Eric Reis might say finding this kind of customer is the sign you need to pivot. That might be true. But it’s not automatically so. You were right enough to get this far, why give up so quickly on your definition of the perfect customer?

Because once you change your direction from focusing on the customer you want to customer you have - that is, once you start focusing on whatever seems to be working rather than your goals - you will quickly find yourself running a different company than you had planned.

This is one of the hardest problems for entrepreneurs: How do you decide when good news is leading to a better place, or a fatal compromise to your dream?

This came up constantly for me while running Puppet, in almost every part of my job. For instance, we did all of our high-growth hiring from Portland. Because of the small market and especially small tech sector, you have to be pragmatic about who you hire. You look up in 2 years, and you’ve got complete teams built off that first compromise hire. That first developer hire was a success, but changed your team’s direction. Now that that person has hired a team, or at least provided the template for the team, are you where you wanted to be, or completely off track?

You can argue that this isn’t a problem we would have had in the Bay Area, but not compromising when hiring is as pernicious a myth as true love. Even if you think someone is perfect, they’re still a real person, with opinions, behaviors, and habits that affect the destiny of your company, in good and bad ways. Again, the point here isn’t that you failed when you hired this person - the point is that the success you experienced changes your destiny in ways you can’t undo or predict.

That first customer started making feature requests and filing bugs, and two years later you’ve built something that can attract a lot more customers like them. Yay, or boo? There’s no right answer. I know one company that was almost destroyed by its biggest customer, and easily lost two years of roadmap progress focusing on them, but I know another company who built an amazing business by initially focusing on a couple of heavyweight customers.

When no decisions have been made, all life in front of you is opportunity. Choosing one opportunity inherently closes off others, and great decision makers know and accept this. If anything, sometimes a decision is great because of the opportunities it rejects, rather than those it selects. Running a startup is an accumulation of these bets, these selections and rejections. It’s hard to predict the consequences of even one of these big decisions, and it’s impossible to know what their legacy will be in a few years. You might end up exactly where you want… but you might be sowing the seeds of your downfall. That’s what makes the job hard.

The only tool I ever developed for managing this over the long term was to hold clearly to a specific set of goals, and to a specific strategy. When successes came along that didn’t fit our goals or strategy, we could decide to change them. But if we chose not to, then we essentially ignored it. It was a false positive. On the other hand, if you look up and a series of false positives starts to look like a trend you want to refocus on, then you must first rethink your goals and strategy, and only then switch your execution.

Or at least, that’s what I did. In truth, though, I don’t think I held the line enough. I think I was too willing to listen to people who were tactically great but didn’t buy into our strategy, and I was too willing to believe someone else’s expertise should take priority over my opinions. That doesn’t mean these were all mistakes, but it does make my time at Puppet easy to see through the lens of compromise and regret rather than the great success it really is.

In the end, when I look at how Puppet evolved, my sharpest pains come from compromises in strategy, and my brightest joys shine from a flawed execution in service of a pure vision.

We’re seeing a rapid separation into SaaS companies and suppliers to SaaS companies

Software as a service is the most important technology business model innovation my lifetime, and before too long all important technology will be either provided as a service, or be an expert tool purchased in order to provide a service.

Software as a Service (SaaS) will not just restructure the entire economy of technology but dramatically improve it, because the rate of learning in a SaaS company is orders of magnitude better than for on-premise software (and oh my god better than for hardware). A really good on-premise software product is released and upgraded quarterly, meaning its fastest learning cycle is a 3 month cycle. A good hosted product is released tens of times a day. Twenty releases a day, times 5 days in a week and 12 weeks in a quarter mean that a SaaS company gets 1200 releases out in the time an on-premise company gets a single release.

Every release is a learning opportunity, which means a SaaS company can learn, and thus improve its user experience, more than 1000x more quickly than even a high-performing traditional software company. With this kind of difference in the rate of improvement, before too long anyone who isn’t a SaaS company is irrelevant. Literally - you can just ignore them.

Except of course that’s not true. If you’re a SaaS company, you can’t rely only on other SaaS companies; there are problems that can’t be solved off-premise, complexities that can’t be abstracted into a service, and technologies needed to provide your service that can’t be procured as a service. The most obvious example is that it all has to hit silicon at some point, and someone somewhere does actually have to buy CPUs, hard drives, and memory, with enough duct tape and baling twine to hold it all together and enough fans to cool it all down. While this is one obvious exception, there are plenty of others.

Thus, even with the learning rate of SaaS companies, and the resulting obsolescence of most other kinds of tech providers, there is still room for those who sell to the SaaS companies. This is somewhat akin to the ‘49 gold rush, when there were gold miners and those who sold to gold miners. Both were valid pursuits, and tightly entwined, but they were very different businesses.

In the early days of the software world, and especially the early days of the enterprise IT department, every company evolved to be mediocre at any kind of technology, and they all looked pretty similar. They could do just about anything, and they sold to pretty much everybody, but they couldn’t do anything particularly well. This impending world of SaaS companies hires only experts; they are absolutely fantastic at everything they care about, and they have an equally fantastic service partner to whom they can outsource anything they don’t care about.

In some ways, this world is even more dangerous to the status quo of the technology industry. Our entire industry has evolved to sell to generalists: We build pretty decent stuff, and sell it to people who are ok at managing it. Our stuff can’t be too great, because great stuff requires expert users, and we can’t demand too much of our users because they have to be competent at nearly everything within a very large field (“networking”, “hardware”, “applications”), so they can’t invest in being particularly excellent at any one thing. Also, great stuff would be really expensive, and companies don’t buy expensive stuff, because no particular investment is all that important to them.

In this new world, there exist only experts, whose entire mandate is to specialize in the specific tools and technologies that will allow a SaaS company to excel, to win. For the investments that can make a real difference, no cost is too high, and no expertise is too narrow. For investments that can’t make a real difference, of course, we just find a service partner who cares so much about it we don’t have to think about it. Thus, as a SaaS company, I only invest in technology when I can afford to invest in experts, and I immediately reject any technology built for generalists because I can’t achieve outsize results with tools built for generalists.

Here we reach a future where there exist only SaaS companies, and companies who build expert tools for narrow use cases that exist within SaaS companies. Anyone else is fiercely riding a bicycle while watching a train recede into the future. The pundits of today are correct that the cloud is the most important trend of the modern era, but they’re wrong in thinking that AWS or Cloud Foundry hold a candle to the restructuring that SaaS will wreak.

Throughout my 12 years as CEO of Puppet, I made critical decisions that affected nearly everyone involved in the company: What investors should we work with? Who should we hire? What products should we build? Should we take that deal with a customer?

These decisions are scary. Getting them right can be a huge lift for a company, but getting them wrong can destroy one. I worked at a company that lost more than a year of growth and roadmap development because they signed their largest customer ever. One of my advisors told me a payment of more than one hundred million dollars from a partner destroyed his company. I’ve also taken big risks on key employees, and had some of them work out fantastically, and others flame out spectacularly.

We’ve all been there. You’re in an interview, the first of many discussions with a candidate. You’re focused on building rapport, getting an overview of their career, but some niggling fears are pushing down on your sense of comfort. What do you do?

I know what I used to do, and it didn’t work. I’d focus initially on an overview and getting to know them, then I’d dive deep into their experience and skills. At that point I would find I still had fundamental questions I need answered, but it’s a bit awkward to ask these basic questions in the 3rd interview, isn’t it? Awkward indeed. You either take the risk and hire without those answers, or you take the loss and reset, looking like an amateur. I’ve done both of these more times than I can count. Just talking about it brings up again the anguish I’ve had in those late meetings, realizing there were critical questions I didn’t have answers to, and I just didn’t know how to get out of there.

So I flipped it. Now I chase the fear. I pin it to the wall, and I scrutinize it until it withers away or takes over the room.

With customers, one of my scariest questions has always been, “Why are you buying our product?”, as if merely asking them would cause them to reconsider. The only thing worse than losing a prospect, though, is having the wrong customer sign up. I’ve had customers who I spent hundreds of thousands of dollars supporting who never succeeded with the product and then didn’t renew a year later. Some have signed up then complained incessantly that we don’t have the features they need. I’ve said yes to a painfully low-priced deal to land a big customer, only to have them change all the terms at literally the last minute because they know they have us over a barrel. These are all signs that someone should not have signed up with you, that you have a mismatch you’re dodging.

I have hired people with time at important companies during critical growth years, and then found out later for all their knowledge of the events, they weren’t remotely involved. That was early in my experience of hiring - I’ve since learned to ask, “Yes, but what exactly did you accomplish while working at that great company near those great people?” I don’t care if a previous employer did great things, I care if you helped them do great things. Otherwise, it’s irrelevant.

This change in practice was initially frightening, and felt almost like I was breaking the social contract. I’m already hard enough to get along with, and not a person people usually enjoy interviewing with. I also have great loss aversion, especially when trying to convince a talented executive to move to Portland. The truth is, though, if you aren’t going to move, I should learn that in our first conversation, not after I’ve put 30 hours into interviewing you only to have to reject the offer because you can’t pull your kids out of school. I understand, but shouldn’t we have learned that together earlier?

Once I forced the conversation through these risky channels, though, the rest of the work is done without much fear. We quashed all the really scary stuff in the beginning. Even better, because we’ve walked the deep dark forest together and come out the other side (assuming we have), we understand each other better, trust each other more, and all of our conversations are tinged with that greater knowledge. When I recently went to hire a CFO, I rejected multiple highly qualified people very quickly because they were looking for the badge of leading an IPO rather than wanting to help build a great company. That’s not necessarily bad, but it’s not what we were looking for.

The best thing about this technique is that it’s good science. I have a chemistry degree, and I really kicked myself when I realized how inconsistent, how unscientific I was when making decisions. In some areas I experience no real loss aversion, so I can dig right into the scary stuff, but in hiring, signing large customers, and a few other areas, I found that the fear had been too strong and I kept tying myself in knots. Once I backed away and considered any candidate, any decision as a hypothesis, and then looked at my questions as experiments trying to disprove that hypothesis, the answer was dispassionately clear: You must run the most dangerous tests first.

Sure, you can do a bunch of simple experiments that are highly unlikely to prove you wrong. But if the 9th test was always the most dangerous one and your hypothesis fails there, then you just wasted a lot of time and paid a high opportunity cost.

The objectivity that came from looking at this problem through a scientific lens helped me recognize that my loss aversion was making my decision making much worse. I didn’t so much learn to operate without fear as learned that the fear was rational and was a useful signal. It gave me the strength to pursue that fear, rather than hide from it, even in the scariest areas.

Maybe you aren’t hiring people, or seeking investors. But I know you make key decisions, decisions that affect you and those around you. If you don’t get out of your comfort zone and ask the important questions, not just the easy ones, then you’re basically filibustering your work: “Well, we’ve run out of time, guess we’ll just have to skip those questions.”

If instead you can find a way to focus first on the scariest parts of those decisions, the parts that can least stand the light, then I think you’ll find you make higher quality decisions, you will enjoy the process more, and you’ll have better outcomes for everyone involved.

Kanban is an incredibly useful productivity tool, initially developed in Japan on automobile manufacturing lines. It has since become a widely adopted practice, including in software development and project management. Or has it?

That cute, simple tool you have that uses post-its, or skeuomorphic representations of them, to keep track of the state of some task or project? It’s not Kanban. To paraphrase Bill Hicks: No no no, I know you think it is. But it’s not.

What you have is a useful means of task and project management. It might be awesome. It might be saving you effort, time, and stress, and actively making your life better. I’m sure it’s a good tool, Brent. But it likely has nothing to do with Kanban.

To be clear, that’s fine. There’s no rule that says Kanban is useful to solving your problem, or that you ever need to use it. It’s just, you know, words have meanings. And the meaning of Kanban is all about inventory management. It’s true that you totally could be using post-its on a whiteboard to track inventory. But you’re probably not.

In both your task tracker and in Kanban, the card represents something. That is, the card itself is not relevant, but it represents a thing that you care about. In your tool, it’s representing some task that someone needs to do and the state that the task is in. This helps you to understand and communicate key information across all of your tasks, projects, and teams. I can see why you find this useful. Heck, I find it useful. I’m using it to track the state of this article, for instance.

In Kanban, the card represents something completely different: The need to refill inventory. At its simplest, you use cards to denote the minimum allowable inventory in a system, such as car doors sitting at an installation station. You do so by literally placing a card on the door at the minimum level. You pair these cards with separate rules about the maximum allowable inventory. Now each of your inventory pools (doors, engines, seats, etc.) have maximum and minimum levels - if the inventory gets low enough, the installer encounters the card and orders a refill, which itself is never above the maximum allowed. As you operate the system you tune it over time to make sure your min and max levels are right.

For most of your work, you can ignore the card and focus on what’s in front of you, but as soon as you encounter it, you must take action. This gives you two features that are otherwise lacking: You get to ignore the card and focus on your work for the majority of the time, which is incredibly important for productivity, and you also get to explicitly separate the process of optimizing the inventory pools from how you consume them. You can always be in the moment when you do the work.

On first blush, you might think to yourself that this doesn’t sound very useful. I mean, how much of your life is really affected by inventory problems? Pffft. Literally all of it. You deal with this constantly in your car, for example; its maker decides on your maximum fuel level (the tank is fixed in size), and you never want to run out of gas, but instead of a card you have a light on your dash when it gets too low. Obviously every grocery store and restaurant has to think about this, but so do banks (envelopes, paper, checks), mechanics (parts, tools), and coffee shops (coffee, chairs - yes, chairs).

You have personal inventory problems, too. We keep hearing about these magical fridges that will order milk for us automatically (but are more likely to be used in a DDoS); Amazon has released one-touch buttons that enable us to trivially order new inventory; and most of us have experienced the ignominy of running out of a key supply at just the wrong time, such as when using the toilet. To see these problems for what they really are, you need to step into a different mental model, a new world.

You need to step into the world of inventory. Rather than seeing everything around you in terms of work to be done, see it in terms of pools of inventory to be shifted, consumed, and refilled. It’s not necessarily “better”, but it is often enlightening. Kanban got created as a tool specifically for increasing the efficiency of such a world, and only makes sense when you’re in it. In fact, the cards themselves aren’t important at all - there are plenty of different triggers available.

It might shock you to realize just how much of your life would be improved by viewing the world this way. Suddenly all of those latent tasks that are sudden emergencies when you run out of something become simple efficiency problems that are easy to model and solve. In my last few years of experimenting with this in my personal life, I’ve built many triggers into many of our inventory pools. None of them are cards, but they are all closer to Kanban than your Trello board.

For example, we go through a lot of granola in my house. Our means of ensuring we never run out is to have two containers, about the same size. We always pour breakfast from one and refill from the other, and the emptying of our refill container is the trigger that causes us to buy more granola.

We keep one in-use and one unused toilet paper roll in each bathroom, plus a cache in a closet. Emptying the in-use triggers using the extra roll, which triggers pulling another roll from the cache. If that is the last roll there, pulling it triggers buying more.

In each of these cases, we’ve set up inventory pools that match how long it takes to refresh them. For example, our granola containers are sized that so that we don’t go through a whole container faster than it takes us to buy more. We never run out of toilet paper, but we don’t have to dedicate a room to storing it.

This perspective also allows us to recognize when we are missing a trigger to refill inventory, allowing us to shift the conversation from personal blame to process improvements. For example, in a bid to teach our kids to self-regulate their sugar intake, we’ve started making our own fruit yogurt and letting them add sugar, rather than buying pre-made fruit+sugar yogurt. We kept running out, though, because it took a day to thaw frozen fruit. We didn’t have an appropriate trigger to start this task at the right time. Having recognized this problem, we created one (I get frozen fruit out to thaw when we have about one meal left of pre-mixed yogurt), and on first blush, it seems to be working. We haven’t yet integrated it with one that buys more yogurt on the right cycle, though, so it’s not yet a complete system.

These are examples of using non-card triggers for Kanban-style inventory management. It’s the triggers that matter, not using cards to represent them. If we tended to have larger collections of unit inventory, cards might be appropriate. E.g., I usually buy razor blades in bulk, and it might be appropriate to label one of those blades with a card to trigger repurchase when I reach it in my stack. Here I’d have to find the right optimization between managing a large inventory, finding the right trigger, and getting the lowest cost per blade (which requires buying in bulk). Combine that with the fact that I usually use an electric razor (which means I rarely assess the state of my blade inventory) and the likelihood of making an inventory mistake goes up, thus increasing the value of a trigger-based system.

For all that I love tools like Trello, and systems like Kanban, I’m not sure they can ever actually be used together. That is, I think we have a whole industry of tools built to model a specific kind of problem, which are instead useful for many things but specifically not the problem they’re meant to exemplify.

The beauty of Kanban is that it’s out in the world, where your work is. (Don’t be confused into thinking that that board or those cards are your work; they just represent it.) I’ve been trying for years to build a Trello board, or some equivalent, that enables an inventory-oriented view on what I’m trying to do, but I’ve not yet succeeded. For instance, [WIP limits] mean something completely different when they represent tasks instead of inventory. That doesn’t mean they’re useless, just not useful for the same reasons.

My recommendation is that you enjoy your task management system, and continue to get what you can out of it. Maybe just stop calling it Kanban. At the same time, though, ask yourself: Where are the inventory pools in my life? What do I run out of, and how can I build triggers at the right point to prevent that? How does my world change when viewed this way?

Most of all, get out into the world. That’s where the work is.