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AI has recently crossed a utility threshold, where cutting-edge models such as GPT-3, Codex, and DALL-E 2 are actually useful and can perform tasks computers cannot do any other way. The act of producing these models is an exploration of a new frontier, with the discovery of unknown capabilities, scientific progress, and incredible product applications as the rewards. And perhaps most exciting for me personally, because the field is fundamentally about creating and studying software systems, great engineers are able to contribute at the same level as great researchers to future progress. “A self-learning AI system.” by DALL-E 2. I first got into software engineering because I wanted to build large-scale systems that could have a direct impact on people’s lives. I attended a math research summer program shortly after I started programming, and my favorite result of the summer was a scheduling app I built for people to book time with the professor. Specifying every detail of how a program should work is hard, and I’d always dreamed of one day putting my effort into hypothetical AI systems that could figure out the details for me. But after taking one look at the state of the art in AI in 2008, I knew it wasn’t going to work any time soon and instead started building infrastructure and product for web startups. DALL-E 2’s rendition of “The two great pillars of the house of artificial intelligence” (which according to my co-founder Ilya Sutskever are great engineering, and great science using this engineering) It’s now almost 15 years later, and the vision of systems which can learn their own solutions to problems is becoming incrementally more real. And perhaps most exciting is the underlying mechanism by which it’s advancing — at OpenAI, and the field generally, precision execution on large-scale models is a force multiplier on AI progress, and we need more people with strong software skills who can deliver these systems. This is because we are building AI models out of unprecedented amounts of compute; these models in turn have unprecedented capabilities, we can discover new phenomena and explore the limits of what these models can and cannot do, and then we use all these learnings to build the next model. “Harnessing the most compute in the known universe” by DALL-E 2 Harnessing this compute requires deep software skills and the right kind of machine learning knowledge. We need to coordinate lots of computers, build software frameworks that allow for hyperoptimization in some cases and flexibility in others, serve these models to customers really fast (which is what I worked on in 2020), and make it possible for a small team to manage a massive system (which is what I work on now). Engineers with no ML background can contribute from the day they join, and the more ML they pick up the more impact they have. The OpenAI environment makes it relatively easy to absorb the ML skills, and indeed, many of OpenAI’s best engineers transferred from other fields. All that being said, AI is not for every software engineer. I’ve seen about a 50-50 success rate of engineers entering this field. The most important determiner is a specific flavor of technical humility. Many dearly-held intuitions from other domains will not apply to ML. The engineers who make the leap successfully are happy to be wrong (since it means they learned something), aren’t afraid not to know something, and don’t push solutions that others resist until they’ve gathered enough intuition to know for sure that it matches the domain. “A beaver who has humbly recently become a machine learning engineer” by DALL-E 2 I believe that AI research is today by far the most impactful place for engineers who want to build useful systems to be working, and I expect this statement to become only more true as progress continues. If you’d like to work on creating the next generation of AI models, email me (gdb@openai.com) with any evidence of exceptional accomplishment in software engineering.
For the first three years of OpenAI, I dreamed of becoming a machine learning expert but made little progress towards that goal. Over the past nine months, I’ve finally made the transition to being a machine learning practitioner. It was hard but not impossible, and I think most people who are good programmers and know (or are willing to learn) the math can do it too. There are many online courses to self-study the technical side, and what turned out to be my biggest blocker was a mental barrier — getting ok with being a beginner again. Studying machine learning during the 2018 holiday season. Early days # A founding principle of OpenAI is that we value research and engineering equally — our goal is to build working systems that solve previously impossible tasks, so we need both. (In fact, our team is comprised of 25% people primarily using software skills, 25% primarily using machine learning skills, and 50% doing a hybrid of the two.) So from day one of OpenAI, my software skills were always in demand, and I kept procrastinating on picking up the machine learning skills I wanted. After helping build OpenAI Gym, I was called to work on Universe. And as Universe was winding down, we decided to start working on Dota — and we needed someone to turn the game into a reinforcement learning environment before any machine learning could begin. Dota # Turning such a complex game into a research environment without source code access was awesome work, and the team’s excitement every time I overcame a new obstacle was deeply validating. I figured out how to break out of the game’s Lua sandbox, LD_PRELOAD in a Go GRPC server to programmatically control the game, incrementally dump the whole game state into a Protobuf, and build a Python library and abstractions with future compatibility for the many different multiagent configurations we might want to use. But I felt half blind. At Stripe, though I gravitated towards infrastructure solutions, I could make changes anywhere in the stack since I knew the product code intimately. In Dota, I was constrained to looking at all problems through a software lens, which sometimes meant I tried to solve hard problems that could be avoided by just doing the machine learning slightly differently. I wanted to be like my teammates Jakub Pachocki and Szymon Sidor, who had made the core breakthrough that powered our Dota bot. They had questioned the common wisdom within OpenAI that reinforcement algorithms didn’t scale. They wrote a distributed reinforcement learning framework called Rapid and scaled it exponentially every two weeks or so, and we never hit a wall with it. I wanted to be able to make critical contributions like that which combined software and machine learning skills. Szymon on the left; Jakub on the right. In July 2017, it looked like I might have my chance. The software infrastructure was stable, and I began work on a machine learning project. My goal was to use behavioral cloning to teach a neural network from human training data. But I wasn’t quite prepared for just how much I would feel like a beginner. I kept being frustrated by small workflow details which made me uncertain if I was making progress, such as not being certain which code a given experiment had used or realizing I needed to compare against a result from last week that I hadn’t properly archived. To make things worse, I kept discovering small bugs that had been corrupting my results the whole time. I didn’t feel confident in my work, but to make it worse, other people did. People would mention how how hard behavioral cloning from human data is. I always made sure to correct them by pointing out that I was a newbie, and this probably said more about my abilities than the problem. It all briefly felt worth it when my code made it into the bot, as Jie Tang used it as the starting point for creep blocking which he then fine-tuned with reinforcement learning. But soon Jie figured out how to get better results without using my code, and I had nothing to show for my efforts. I never tried machine learning on the Dota project again. Time out # After we lost two games in The International in 2018, most observers thought we’d topped out what our approach could do. But we knew from our metrics that we were right on the edge of success and mostly needed more training. This meant the demands on my time had relented, and in November 2018, I felt I had an opening to take a gamble with three months of my time. Team members in high spirits after losing our first game at The International. I learn best when I have something specific in mind to build. I decided to try building a chatbot. I started self-studying the curriculum we developed for our Fellows program, selecting only the NLP-relevant modules. For example, I wrote and trained an LSTM language model and then a Transformer-based one. I also read up on topics like information theory and read many papers, poring over each line until I fully absorbed it. It was slow going, but this time I expected it. I didn’t experience flow state. I was reminded of how I’d felt when I just started programming, and I kept thinking of how many years it had taken to achieve a feeling of mastery. I honestly wasn’t confident that I would ever become good at machine learning. But I kept pushing because… well, honestly because I didn’t want to be constrained to only understanding one part of my projects. I wanted to see the whole picture clearly. My personal life was also an important factor in keeping me going. I’d begun a relationship with someone who made me feel it was ok if I failed. I spent our first holiday season together beating my head against the machine learning wall, but she was there with me no matter how many planned activities it meant skipping. One important conceptual step was overcoming a barrier I’d been too timid to do with Dota: make substantive changes to someone else’s machine learning code. I fine-tuned GPT-1 on chat datasets I’d found, and made a small change to add my own naive sampling code. But it became so painfully slow as I tried to generate longer messages that my frustration overwhelmed my fear, and I implemented GPU caching — a change which touched the entire model. I had to try a few times, throwing out my changes as they exceeded the complexity I could hold in my head. By the time I got it working a few days later, I realized I’d learned something that I would have previously thought impossible: I now understood how the whole model was put together, down to small stylistic details like how the codebase elegantly handles TensorFlow variable scopes. Retooled # After three months of self-study, I felt ready to work on an actual project. This was also the first point where I felt I could benefit from the many experts we have at OpenAI, and I was delighted when Jakub and my co-founder Ilya Sutskever agreed to advise me. Ilya singing karaoke at our company offsite. We started to get very exciting results, and Jakub and Szymon joined the project full-time. I feel proud every time I see a commit from them in the machine learning codebase I’d started. I’m starting to feel competent, though I haven’t yet achieved mastery. I’m seeing this reflected in the number of hours I can motivate myself to spend focused on doing machine learning work — I’m now around 75% of the number of coding hours from where I’ve been historically. But for the first time, I feel that I’m on trajectory. At first, I was overwhelmed by the seemingly endless stream of new machine learning concepts. Within the first six months, I realized that I could make progress without constantly learning entirely new primitives. I still need to get more experience with many skills, such as initializing a network or setting a learning rate schedule, but now the work feels incremental rather than potentially impossible. From our Fellows and Scholars programs, I’d known that software engineers with solid fundamentals in linear algebra and probability can become machine learning engineers with just a few months of self study. But somehow I’d convinced myself that I was the exception and couldn’t learn. But I was wrong — even embedded in the middle of OpenAI, I couldn’t make the transition because I was unwilling to become a beginner again. You’re probably not an exception either. If you’d like to become a deep learning practitioner, you can. You need to give yourself the space and time to fail. If you learn from enough failures, you’ll succeed — and it’ll probably take much less time than you expect. At some point, it does become important to surround yourself by existing experts. And that is one place where I’m incredibly lucky. If you’re a great software engineer who reaches that point, keep in mind there’s a way you can be surrounded by the same people as I am — apply to OpenAI!
The text of my speech introducing OpenAI Five at Saturday’s OpenAI Five Finals event, where our AI beat the world champions at Dota 2: “Welcome everyone. This is an exciting day. First, this is an historic moment: this will be the first time that an AI has even attempted to play the world champions in an esports game. OG is simply on another level relative to other teams we’ve played. So we don’t know what’s going to happen, but win or lose, these will be games to remember. And you know, OpenAI Five and DeepMind’s very impressive StarCraft bot This event is really about something bigger than who wins or loses: letting people connect with the strange, exotic, yet tangible intelligences produced by today’s rapidly progressing AI technology. We’re all used to computer programs which have been meticulously coded by a human programmer. Do one thing that the human didn’t anticipate, and the program will break. We think of our computers as unthinking machines which can’t innovate, can’t be creative, can’t truly understand. But to play Dota, you need to do all these things. So we needed to do something different. OpenAI Five is powered by deep reinforcement learning — meaning that we didn’t code in how to play Dota. We instead coded in the how to learn. Five tries out random actions, and learns from a reward or punishment. In its 10 months of training, its experienced 45,000 years of Dota gameplay against itself. The playstyle it has devised are its own — they are truly creative and dreamed up by our computer — and so from Five’s perspective, today’s games are going to its first encounter with an alien intelligence (no offense to OG!). The beauty of this technology is that our learning code doesn’t know it’s meant for Dota. That makes it general purpose with amazing potential to benefit our lives. Last year we used it to control a robotic hand that no one could program. And we expect to see similar technology in new interactive systems, from elderly care robots to creative assistants to other systems we can’t dream of yet. This is the final public event for OpenAI Five, but we expect to do other Dota projects in the future. I want to thank the incredible team at OpenAI, everyone who worked directly on this project or cheered us on. I want to thank those who have supported the project: Valve, dozens of test teams, today’s casters, and yes, even all the commenters on Reddit. And I want to give massive thanks today to our fantastic guests OG who have taken time out of their tournament schedule to be here today. I hope you enjoy the show — and just to keep things in perspective, no matter how surprising the AIs are to us, know that we’re even more surprising to them!”
This post is co-written by Greg Brockman (left) and Ilya Sutskever (right). We’ve been working on OpenAI for the past three years. Our mission is to ensure that artificial general intelligence (AGI) — which we define as automated systems that outperform humans at most economically valuable work — benefits all of humanity. Today we announced a new legal structure for OpenAI, called OpenAI LP, to better pursue this mission — in particular to raise more capital as we attempt to build safe AGI and distribute its benefits. In this post, we’d like to help others understand how we think about this mission. Why now? # The founding vision of the field of AI was “… to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it”, and to eventually build a machine that thinks — that is, an AGI. But over the past 60 years, progress stalled multiple times and people started thinking of AI as a field that wouldn’t deliver. Since 2012, deep learning has generated sustained progress in many domains using a small simple set of tools, which have the following properties: Generality: deep learning tools are simple, yet they apply to many domains, such as vision, speech recognition, speech synthesis, text synthesis, image synthesis, translation, robotics, and game playing. Competence: today, the only way to get competitive results on most “AI-type problems” is through the use of deep learning techniques. Scalability: good old fashioned AI was able to produce exciting demos, but its techniques had difficulty scaling to harder problems. But in deep learning, more computational power and more data leads to better results. It has also proven easy (if costly) to rapidly increase the amount of compute productively used by deep learning experiments. The rapid progress of useful deep learning systems with these properties makes us feel that it’s reasonable to start taking AGI seriously — though it’s hard to know how far away it is. The impact of AGI # Just like a computer today, an AGI will be applicable to a wide variety of tasks — and just like computers in 1900 or the Internet in 1950, it’s hard to describe (or even predict) the kind of impact AGI will have. But to get a sense, imagine a computer system which can do the following activities with minimal human input: Make a scientific breakthrough at the level of the best scientists Productize that breakthrough and build a company, with a skill comparable to the best entrepreneurs Rapidly grow that company and manage it at large scale The upside of such a computer system is enormous — for an illustrative example, an AGI following the pattern above could produce amazing healthcare applications deployed at scale. Imagine a network of AGI-powered computerized doctors that accumulates a superhuman amount of clinical experience, allowing it to produce excellent diagnoses, deeply understand the nuanced effect of various treatments in lots of conditions, and greatly reduce the human error factor of healthcare — all for very low cost and accessible to everyone. Risks # We already live in a world with entities that surpass individual human abilities, which we call companies. If working on the right goals in the right way, companies can produce huge amounts of value and improve lives. But if not properly checked, they can also cause damage, like logging companies that cut down rain forests, cigarette companies that get children smoking, or scams like Ponzi schemes. We think of AGI as being like a hyper-effective company, with commensurate benefits and risks. We are concerned about AGI pursuing goals misspecified by its operator, malicious humans subverting a deployed AGI, or an out-of-control economy that grows without resulting in improvements to human lives. And because it’s hard to change powerful systems — just think about how hard it’s been to add security to the Internet — once they’ve been deployed, we think it’s important to address AGI’s safety and policy risks before it is created. OpenAI’s mission is to figure out how to get the benefits of AGI and mitigate the risks — and make sure those benefits accrue to all of humanity. The future is uncertain, and there are many ways in which our predictions could be incorrect. But if they turn out to be right, this mission will be critical. If you’d like to work on this mission, we’re hiring! About us # Ilya: I’ve been working on deep learning for 16 years. It was fun to witness deep learning transform from being a marginalized subfield of AI into one the most important family of scientific advances in recent history. As deep learning was getting more powerful, I realized that AGI might become a reality on a timescale relevant to my lifetime. And given AGI’s massive upside and significant risks, I want to maximize the positive parts of this impact and minimize the negative. Greg: Technology causes change, both positive and negative. AGI is the most extreme kind of technology that humans will ever create, with extreme upside and downside. I work on OpenAI because making AGI go well is the most important problem I can imagine contributing towards. Today I try to spend most of my time on technical work, and also work to spark better public discourse about AGI and related topics.
The text of my speech introducing OpenAI Five at yesterday’s Benchmark event: “We’re here to watch humans and AI play Dota, but today’s match will have implications for the world. OpenAI’s mission is to ensure that when we can build machines as smart as humans, they will benefit all of humanity. That means both pushing the limits of what’s possible and ensuring future systems are safe and aligned with human values. We work on Dota because it is a great training ground for AI: it is one of the most complicated games, involving teamwork, real time strategy, imperfect information, and an astronomical combinations of heroes and items. We can’t program a solution, so Five learns by playing 180 years of games against itself every day — sadly that means we can’t learn from the players up here unless they played for a few decades. It’s powered by 5 artificial neural networks which act like an artificial intuition. Five’s neural networks are about the size of the brain of an ant — still far from what we all have in our heads. One year ago, we beat the world’s top professionals at 1v1 Dota. People thought 5v5 would be totally out of reach. 1v1 requires mechanics and positioning; people did not expect the same system to learn strategy. But our AI system can learn problems it was not even designed to solve — we just used the same technology to learn to control a robotic hand — something no one could program. The computational power for OpenAI Five would have been impractical two years ago. But the availability of computation for AI has been increasing exponentially, doubling every 3.5 months since 2012, and one day technologies like this will become commonplace. Feel free to root for either team. Either way, humanity wins.” I’m very excited to see where the upcoming months of OpenAI Five development and testing take us.
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This is re-post of How to Permanently Increase Your Sales by 50% or More in Only One Day article by Steve Pavlina Of all the things you can do to increase your sales, one of the highest leverage activities is attempting to increase your products’ registration rate. Increasing your registration rate from 1.0% to 1.5% means that you simply convince one more downloader out of every 200 to make the decision to buy. Yet that same tiny increase will literally increase your sales by a full 50%. If you’re one of those developers who simply slapped the ubiquitous 30-day trial incentive on your shareware products without going any further than that, then I think a 50% increase in your registration rate is a very attainable goal you can achieve if you spend just one full day of concentrated effort on improving your product’s ability to sell. My hope is that this article will get you off to a good start and get you thinking more creatively. And even if you fail, your result might be that you achieve only a 25% or a 10% increase. How much additional money would that represent to you over the next five years of sales? What influence, if any, did the title of this article have on your decision to read it? If I had titled this article, “Registration Incentives,” would you have been more or less likely to read it now? Note that the title expresses a specific and clear benefit to you. It tells you exactly what you can expect to gain by reading it. Effective registration incentives work the same way. They offer clear, specific benefits to the user if a purchase is made. In order to improve your registration incentives, the first thing you need to do is to adopt some new beliefs that will change your perspective. I’m going to introduce you to what I call the “lies of success” in the shareware industry. These are statements that are not true at all, but if you accept them as true anyway, you’ll achieve far better results than if you don’t. Rule 1: What you are selling is merely the difference between the shareware and the registered versions, not the registered version itself. Note that this is not a true statement, but if you accept it as true, you’ll immediately begin to see the weaknesses in your registration incentives. If there are few additional benefits for buying the full version vs. using the shareware version, then you aren’t offering the user strong enough incentives to make the full purchase. Rule 2: The sole purpose of the shareware version is to close the sale. This is our second lie of success. Note the emphasis on the word “close.” Your shareware version needs to act as a direct sales vehicle. It must be able to take the user all the way to the point of purchase, i.e. your online order form, ideally with nothing more than a few mouse clicks. Anything that detracts from achieving a quick sale is likely to hurt sales. Rule 3: The customer’s perspective is the only one that matters. Defy this rule at your peril. Customers don’t care that you spent 2000 hours creating your product. Customers don’t care that you deserve the money for your hard work. Customers don’t care that you need to do certain things to prevent piracy. All that matters to them are their own personal wants and needs. Yes, these are lies of success. Some customers will care, but if you design your registration incentives assuming they only care about their own self-interests, your motivation to buy will be much stronger than if you merely appeal to their sense of honesty, loyalty, or honor. Assume your customers are all asking, “What’s in it for me if I choose to buy? What will I get? How will this help me?” I don’t care if you’re selling to Fortune 500 companies. At some point there will be an individual responsible for causing the purchase to happen, and that individual is going to consider how the purchase will affect him/her personally: “Will this purchase get me fired? Will it make me look good in front of my peers? Will this make my job easier or harder?” Many shareware developers get caught in the trap of discriminating between honest and dishonest users, believing that honest users will register and dishonest ones won’t. This line of thinking will ultimately get you nowhere, and it violates the third lie of success. When you make a purchase decision, how often do you use honesty as the deciding factor? Do you ever say, “I will buy this because I’m honest?” Or do you consider other more selfish factors first, such as how it will make you feel to purchase the software? The truth is that every user believes s/he is honest, so no user applies the honesty criterion when making a purchase decision. Thinking of your users in terms of honest ones vs. dishonest ones is a complete waste of time because that’s not how users primarily view themselves. Rule 4: Customers buy on emotion and justify with fact. If you’re honest with yourself, you’ll see that this is how you make most purchase decisions. Remember the last time you bought a computer. Is it fair to say that you first became emotionally attached to the idea of owning a new machine? For me, it’s the feeling of working faster, owning the latest technology, and being more productive that motivates me to go computer shopping. Once I’ve become emotionally committed, the justifications follow: “It’s been two years since I’ve upgraded, it will pay for itself with the productivity boost I gain, I can easily afford it, I’ve worked hard and I deserve a new machine, etc.” You use facts to justify the purchase. Once you understand how purchase decisions are made, you can see that your shareware products need to first get the user emotionally invested in the purchase, and then you give them all the facts they need to justify it. Now that we’ve gotten these four lies of success out of the way, let’s see how we might apply them to create some compelling registration incentives. Let’s start with Rule 1. What incentives can be spawned from this rule? The common 30-day trial is one obvious derivative. If you are only selling the difference between the shareware and registered versions, then a 30-day trial implies that you are selling unlimited future days of usage of the program after the trial period expires. This is a powerful incentive, and it’s been proven effective for products that users will continue to use month after month. 30-day trials are easy for users to understand, and they’re also easy to implement. You could also experiment with other time periods such as 10 days, 14 days, or 90 days. The only way of truly knowing which will work best for your products is to experiment. But let’s see if we can move a bit beyond the basic 30-day trial here by mixing in a little of Rule 3. How would the customer perceive a 30-day trial? In most cases 30 days is plenty of time to evaluate a product. But in what situations would a 30-day trial have a negative effect? A good example is when the user downloads, installs, and briefly checks out a product s/he may not have time to evaluate right away. By the time the user gets around to fully evaluating it, the shareware version has already expired, and a sale may be lost as a result. To get around this limitation, many shareware developers have started offering 30 days of actual program usage instead of 30 consecutive days. This allows the user plenty of time to try out the program at his/her convenience. Another possibility would be to limit the number of times the program can be run. The basic idea is that you are giving away limited usage and selling unlimited usage of the program. This incentive definitely works if your product is one that will be used frequently over a long period of time (much longer than the trial period). The flip side of usage limitation is to offer an additional bonus for buying within a certain period of time. For instance, in my game Dweep, I offer an extra 5 free bonus levels to everyone who buys within the first 10 days. In truth I give the bonus levels to everyone who buys, but the incentive is real from the customer’s point of view. Remember Rule 3 - it doesn’t matter what happens on my end; it only matters what the customer perceives. Any customer that buys after the first 10 days will be delighted anyway to receive a bonus they thought they missed. So if your product has no time-based incentives at all, this is the first place to start. When would you pay your bills if they were never due, and no interest was charged on late payments? Use time pressure to your advantage, either by disabling features in the shareware version after a certain time or by offering additional bonuses for buying sooner rather than later. If nothing else and if it’s legal in your area, offer a free entry in a random monthly drawing for a small prize, such as one of your other products, for anyone who buys within the first X days. Another logical derivative of Rule 1 is the concept of feature limitation. On the crippling side, you can start with the registered version and begin disabling functionality to create the shareware version. Disabling printing in a shareware text editor is a common strategy. So is corrupting your program’s output with a simple watermark. For instance, your shareware editor could print every page with your logo in the background. Years ago the Association of Shareware Professionals had a strict policy against crippling, but that policy was abandoned, and crippling has been recognized as an effective registration incentive. It is certainly possible to apply feature limitation without having it perceived as crippling. This is especially easy for games, which commonly offer a limited number of playable levels in the shareware version with many more levels available only in the registered version. In this situation you offer the user a seemingly complete experience of your product in the shareware version, and you provide additional features on top of that for the registered version. Time-based incentives and feature-based incentives are perhaps the two most common strategies used by shareware developers for enticing users to buy. Which will work best for you? You will probably see the best results if you use both at the same time. Imagine you’re the end user for a moment. Would you be more likely to buy if you were promised additional features and given a deadline to make the decision? I’ve seen several developers who were using only one of these two strategies increase their registration rates dramatically by applying the second strategy on top of the first. If you only use time-based limitations, how could you apply feature limitation as well? Giving the user more reasons to buy will translate to more sales per download. One you have both time-based and feature-based incentives to buy, the next step is to address the user’s perceived risk by applying a risk-reversal strategy. Fortunately, the shareware model already reduces the perceived risk of purchasing significantly, since the user is able to try before buying. But let’s go a little further, keeping Rule 3 in mind. What else might be a perceived risk to the user? What if the user reaches the end of the trial period and still isn’t certain the product will do what s/he needs? What if the additional features in the registered version don’t work as the user expects? What can we do to make the decision to purchase safer for the user? One approach is to offer a money-back guarantee. I’ve been offering a 60-day unconditional money-back guarantee on all my products since January 2000. If someone asks for their money back for any reason, I give them a full refund right away. So what is my return rate? Well, it’s about 8%. Just kidding! Would it surprise you to learn that my return rate at the time of this writing is less than 0.2%? Could you handle two returns out of every 1000 sales? My best estimate is that this one technique increased my sales by 5-10%, and it only took a few minutes to implement. When I suggest this strategy to other shareware developers, the usual reaction is fear. “But everyone would rip me off,” is a common response. I suggest trying it for yourself on an experimental basis; a few brave souls have already tried it and are now offering money-back guarantees prominently. Try putting it up on your web site for a while just to convince yourself it works. You can take it down at any time. After a few months, if you’re happy with the results, add the guarantee to your shareware products as well. I haven’t heard of one bad outcome yet from those who’ve tried it. If you use feature limitation in your shareware products, another important component of risk reversal is to show the user exactly what s/he will get in the full version. In Dweep I give away the first five levels in the demo version, and purchasing the full version gets you 147 more levels. When I thought about this from the customer’s perspective (Rule 3), I realized that a perceived risk is that s/he doesn’t know if the registered version levels will be as fun as the demo levels. So I released a new demo where you can see every level but only play the first five. This lets the customer see all the fun that awaits them. So if you have a feature-limited product, show the customer how the feature will work. For instance, if your shareware version has printing disabled, the customer could be worried that the full version’s print capability won’t work with his/her printer or that the output quality will be poor. A better strategy is to allow printing, but to watermark the output. This way the customer can still test and verify the feature, and it doesn’t take much imagination to realize what the output will look like without the watermark. Our next step is to consider Rule 2 and include the ability close the sale. It is imperative that you include an “instant gratification” button in your shareware products, so the customer can click to launch their default web browser and go directly to your online order form. If you already have a “buy now” button in your products, go a step further. A small group of us have been finding that the more liberally these buttons are used, the better. If you only have one or two of these buttons in your shareware program, you should increase the count by at least an order of magnitude. The current Dweep demo now has over 100 of these buttons scattered throughout the menus and dialogs. This makes it extremely easy for the customer to buy, since s/he never has to hunt around for the ordering link. What should you label these buttons? “Buy now” or “Register now” are popular, so feel free to use one of those. I took a slightly different approach by trying to think like a customer (Rule 3 again). As a customer the word “buy” has a slightly negative association for me. It makes me think of parting with my cash, and it brings up feelings of sacrifice and pressure. The words “buy now” imply that I have to give away something. So instead, I use the words, “Get now.” As a customer I feel much better about getting something than buying something, since “getting” brings up only positive associations. This is the psychology I use, but at present, I don’t know of any hard data showing which is better. Unless you have a strong preference, trust your intuition. Make it as easy as possible for the willing customer to buy. The more methods of payment you accept, the better your sales will be. Allow the customer to click a button to print an order form directly from your program and mail it with a check or money order. On your web order form, include a link to a printable text order form for those who are afraid to use their credit cards online. If you only accept two or three major credit cards, sign up with a registration service to handle orders for those you don’t accept. So far we’ve given the customer some good incentives to buy, minimized perceived risk, and made it easy to make the purchase. But we haven’t yet gotten the customer emotionally invested in making the purchase decision. That’s where Rule 4 comes in. First, we must recognize the difference between benefits and features. We need to sell the sizzle, not the steak. Features describe your product, while benefits describe what the user will get by using your product. For instance, a personal information manager (PIM) program may have features such as daily, weekly, and monthly views; task and event timers; and a contact database. However, the benefits of the program might be that it helps the user be more organized, earn more money, and enjoy more free time. For a game, the main benefit might be fun. For a nature screensaver, it could be relaxation, beauty appreciation, or peace. Features are logical; benefits are emotional. Logical features are an important part of the sale, but only after we’ve engaged the customer’s emotions. Many products do a fair job of getting the customer emotionally invested during the trial period. If you have an addictive program or one that’s fun to use, such as a game, you may have an easy time getting the customer emotionally attached to using it because the experience is already emotional in nature. But whatever your product is, you can increase your sales by clearly illustrating the benefits of making the purchase. A good place to do this is in your nag screens. I use nag screens both before and after the program runs to remind the user of the benefits of buying the full version. At the very least, include a nag screen when the customer exits the program, so the last thing s/he sees will be a reminder of the product’s benefits. Take this opportunity to sell the user on the product. Don’t expect features like “customizable colors” to motivate anyone to buy. Paint a picture of what benefits the user will obtain with the full version. Will I save time? Will I have more fun? Will I live longer, save money, or feel better? The simple change from feature-oriented selling to benefit-oriented selling can easily double or triple your sales. Be sure to use this approach on your web site as well if you don’t already. Developers who’ve recently made the switch have been reporting some amazing results. If you’re drawing a blank when trying to come up with benefits for your products, the best thing you can do is to email some of your old customers and ask them why they bought your program. What did it do for them? I’ve done this and was amazed at the answers I got back. People were buying my games for reasons I’d never anticipated, and that told me which benefits I needed to emphasize in my sales pitch. The next key is to make your offer irresistible to potential customers. Find ways to offer the customer so much value that it would be harder to say no than to say yes. Take a look at your shareware product as if you were a potential customer who’d never seen it before. Being totally honest with yourself, would you buy this program if someone else had written it? If not, don’t stop here. As a potential customer, what additional benefits or features would put you over the top and convince you to buy? More is always better than less. In the original version of Dweep, I offered ten levels in the demo and thirty in the registered version. Now I offer only five demo levels and 152 in the full version, plus a built-in level editor. Originally, I offered the player twice the value of the demo; now I’m offering over thirty times the value. I also offer free hints and solutions to every level; the benefit here is that it minimizes player frustration. As I keep adding bonuses for purchasing, the offer becomes harder and harder to resist. What clever bonuses can you throw in for registering? Take the time to watch an infomercial. Notice that there is always at least one “FREE” bonus thrown in. Consider offering a few extra filters for an image editor, ten extra images for a screensaver, or extra levels for a game. What else might appeal to your customers? Be creative. Your bonus doesn’t even have to be software-based. Offer a free report about building site traffic with your HTML editor, include an essay on effective time management with your scheduling program, or throw in a small business success guide with your billing program. If you make such programs, you shouldn’t have too much trouble coming up with a few pages of text that would benefit your customers. Keep working at it until your offer even looks irresistible to you. If all the bonuses you offer can be delivered electronically, how many can you afford to include? If each one only gains one more customer in a thousand (0.1%), would it be worth the effort over the lifetime of your sales? So how do you know if your registration incentives are strong enough? And how do you know if your product is over-crippled? Where do you draw the line? These are tough issues, but there is a good way to handle them if your product is likely to be used over a long period of time, particularly if it’s used on a daily basis. Simply make your program gradually increase its registration incentives over time. One easy way to do this is with a delay timer on your nag screens that increases each time the program is run. Another approach is to disable certain features at set intervals. You begin by disabling non-critical features and gradually move up to disabling key functionality. The program becomes harder and harder to continue using for free, so the benefits of registering become more and more compelling. Instead of having your program completely disable itself after your trial period, you gradually degrade its usability with additional usage. This approach can be superior to a strict 30-day trial, since it allows your program to still be used for a while, but after prolonged usage it becomes effectively unusable. However, you don’t simply shock the user by taking away all the benefits s/he has become accustomed to on a particular day. Instead, you begin with a gentle reminder that becomes harder and harder to ignore. There may be times when your 30-day trial shuts off at an inconvenient time for the user, and you may lose a sale as a result. For instance, the user may not have the money at the time, or s/he may be busy at the trial’s end and forget to register. In that case s/he may quickly replace what was lost with a competitor’s trial version. The gradual degradation approach allows the user to continue using your product, but with increasing difficulty over time. Eventually, there is a breaking point where the user either decides to buy or to stop using the program completely, but this can be done within a window of time at the user’s convenience. Hopefully this article has gotten you thinking creatively about all the overlooked ways you can entice people to buy your shareware products. The most important thing you can do is to begin seeing your products through your customers’ eyes. What additional motivation would convince you to buy? What would represent an irresistible offer to you? There is no limit to how many incentives you can add. Don’t stop at just one or two; instead, give the customer a half dozen or more reasons to buy, and you’ll see your registration rate soar. Is it worth spending a day to do this? I think so.
I'm a big (neo)vim buff. My config is over 1500 lines and I regularly write new scripts. I recently ported my neovim config to a new laptop. Before then, I was using VSCode to write, and when I switched back I immediately saw a big gain in productivity. People often pooh-pooh vim (and other assistive writing technologies) by saying that writing code isn't the bottleneck in software development. Reading, understanding, and thinking through code is! Now I don't know how true this actually is in practice, because empirical studies of time spent coding are all over the place. Most of them, like this study, track time spent in the editor but don't distinguish between time spent reading code and time spent writing code. The only one I found that separates them was this study. It finds that developers spend only 5% of their time editing. It also finds they spend 14% of their time moving or resizing editor windows, so I don't know how clean their data is. But I have a bigger problem with "writing is not the bottleneck": when I think of a bottleneck, I imagine that no amount of improvement will lead to productivity gains. Like if a program is bottlenecked on the network, it isn't going to get noticeably faster with 100x more ram or compute. But being able to type code 100x faster, even with without corresponding improvements to reading and imagining code, would be huge. We'll assume the average developer writes at 80 words per minute, at five characters a word, for 400 characters a minute.What could we do if we instead wrote at 8,000 words/40k characters a minute? Writing fast Boilerplate is trivial Why do people like type inference? Because writing all of the types manually is annoying. Why don't people like boilerplate? Because it's annoying to write every damn time. Programmers like features that help them write less! That's not a problem if you can write all of the boilerplate in 0.1 seconds. You still have the problem of reading boilerplate heavy code, but you can use the remaining 0.9 seconds to churn out an extension that parses the file and presents the boilerplate in a more legible fashion. We can write more tooling This is something I've noticed with LLMs: when I can churn out crappy code as a free action, I use that to write lots of tools that assist me in writing good code. Even if I'm bottlenecked on a large program, I can still quickly write a script that helps me with something. Most of these aren't things I would have written because they'd take too long to write! Again, not the best comparison, because LLMs also shortcut learning the relevant APIs, so also optimize the "understanding code" part. Then again, if I could type real fast I could more quickly whip up experiments on new apis to learn them faster. We can do practices that slow us down in the short-term Something like test-driven development significantly slows down how fast you write production code, because you have to spend a lot more time writing test code. Pair programming trades speed of writing code for speed of understanding code. A two-order-of-magnitude writing speedup makes both of them effectively free. Or, if you're not an eXtreme Programming fan, you can more easily follow the The Power of Ten Rules and blanket your code with contracts and assertions. We could do more speculative editing This is probably the biggest difference in how we'd work if we could write 100x faster: it'd be much easier to try changes to the code to see if they're good ideas in the first place. How often have I tried optimizing something, only to find out it didn't make a difference? How often have I done a refactoring only to end up with lower-quality code overall? Too often. Over time it makes me prefer to try things that I know will work, and only "speculatively edit" when I think it be a fast change. If I could code 100x faster it would absolutely lead to me trying more speculative edits. This is especially big because I believe that lots of speculative edits are high-risk, high-reward: given 50 things we could do to the code, 49 won't make a difference and one will be a major improvement. If I only have time to try five things, I have a 10% chance of hitting the jackpot. If I can try 500 things I will get that reward every single time. Processes are built off constraints There are just a few ideas I came up with; there are probably others. Most of them, I suspect, will share the same property in common: they change the process of writing code to leverage the speedup. I can totally believe that a large speedup would not remove a bottleneck in the processes we currently use to write code. But that's because those processes are developed work within our existing constraints. Remove a constraint and new processes become possible. The way I see it, if our current process produces 1 Utils of Software / day, a 100x writing speedup might lead to only 1.5 UoS/day. But there are other processes that produce only 0.5 UoS/d because they are bottlenecked on writing speed. A 100x speedup would lead to 10 UoS/day. The problem with all of this that 100x speedup isn't realistic, and it's not obvious whether a 2x improvement would lead to better processes. Then again, one of the first custom vim function scripts I wrote was an aid to writing unit tests in a particular codebase, and it lead to me writing a lot more tests. So maybe even a 2x speedup is going to be speed things up, too. Patreon Stuff I wrote a couple of TLA+ specs to show how to model fork-join algorithms. I'm planning on eventually writing them up for my blog/learntla but it'll be a while, so if you want to see them in the meantime I put them up on Patreon.
Here’s Jony Ive in his Stripe interview: What we make stands testament to who we are. What we make describes our values. It describes our preoccupations. It describes beautiful succinctly our preoccupation. I’d never really noticed the connection between these two words: occupation and preoccupation. What comes before occupation? Pre-occupation. What comes before what you do for a living? What you think about. What you’re preoccupied with. What you think about will drive you towards what you work on. So when you’re asking yourself, “What comes next? What should I work on?” Another way of asking that question is, “What occupies my thinking right now?” And if what you’re occupied with doesn’t align with what you’re preoccupied with, perhaps it's time for a change. Email · Mastodon · Bluesky
There's no country on earth that does hype better than America. It's one of the most appealing aspects about being here. People are genuinely excited about the future and never stop searching for better ways to work, live, entertain, and profit. There's a unique critical mass in the US accelerating and celebrating tomorrow. The contrast to Europe couldn't be greater. Most Europeans are allergic to anything that even smells like a commercial promise of a better tomorrow. "Hype" is universally used as a term to ridicule anyone who dares to be excited about something new, something different. Only a fool would believe that real progress is possible! This is cultural bedrock. The fault lines have been settling for generations. It'll take an earthquake to move them. You see this in AI, you saw it in the Internet. Europeans are just as smart, just as inventive as their American brethren, but they don't do hype, so they're rarely the ones able to sell the sizzle that public opinion requires to shift its vision for tomorrow. To say I have a complicated relationship with venture capital is putting it mildly. I've spent a career proving the counter narrative. Proving that you can build and bootstrap an incredible business without investor money, still leave a dent in the universe, while enjoying the spoils of capitalism. And yet... I must admit that the excesses of venture capital are integral to this uniquely American advantage on hype. The lavish overspending during the dot-com boom led directly to a spectacular bust, but it also built the foundation of the internet we all enjoy today. Pets.com and Webvan flamed out such that Amazon and Shopify could transform ecommerce out of the ashes. We're in the thick of peak hype on AI right now. Fantastical sums are chasing AGI along with every dumb derivative mirage along the way. The most outrageous claims are being put forth on the daily. It's easy to look at that spectacle with European eyes and roll them. Some of it is pretty cringe! But I think that would be a mistake. You don't have to throw away your critical reasoning to accept that in the face of unknown potential, optimism beats pessimism. We all have to believe in something, and you're much better off believing that things can get better than not. Americans fundamentally believe this. They believe the hype, so they make it come to fruition. Not every time, not all of them, but more of them, more of the time than any other country in the world. That really is exceptional.
I’m working on a Go library appendstore for append-only store of lots of things in a single file. To make things as robust as possible I was calling os.File.Sync() after each append. Sync() is waiting until the data is acknowledged as truly, really written to disk (as opposed to maybe floating somewhere in disk drive’s write buffer). Oh boy, is it slow. A test of appending 1000 records would take over 5 seconds. After removing the Sync() it would drop to 5 milliseconds. 1000x faster. I made sync optional - it’s now up to the user of the library to pick it, defaults to non-sync. Is it unsafe now? Well, the reality is that it probably doesn’t matter. I don’t think lots of software does the sync due to slowness and the world still runs.
