Cover photo for Philip I. Thomas
I just returned from two weeks of "slow travel" in Paris. Instead of visiting museums and attractions, "slow travel" consists of living my everyday life, but in a different city. My trip generally consisted of working during the day, then going out to dinner at night. Every afternoon, I would take a break from work and walk to explore a local coffee shop. Throughout the trip, I visited numerous cafes and had much delicious coffee. But, one place stood out as particularly special - Substance Cafe. Located in the heart of Paris, Substance Cafe is the brainchild of Joachim, a competition barista who has transformed his passion for coffee into a unique and captivating experience.     I am consistently captivated by independent creators operating at their peak potential. Joachim is one of those people. He flies around the world to participate in coffee competitions, then returns to Substance Cafe as his workshop to train and hone his skills before the next event. He operates the cafe mostly alone, and has worked meticulously to control and optimize every step of coffee-making from the farm to cup. He sources and roasts the beans himself, formulates custom water recipes, and rebuilds his equipment in pursuit of the perfect coffee. At Substance Cafe, guests are offered a front-row seat to Joachim's creative pursuit. Substance Cafe is a far cry from your typical neighborhood coffee shop. You won't find people queuing for their morning caffeine fix, as the doors only open at noon. In place of tables, takeaway cups, and sugar, you'll discover 15 stools at a bar arranged around the espresso machine, inviting guests to become an audience to the coffee-making process. While a concise standard menu of familiar cafe drinks is available, the pièce de résistance at Substance Cafe is the Omakase experience. Joachim curates a symphony of flavors through a rotating selection of a dozen specialty coffee beans, personally chosen to transport patrons on a sensory tour of the coffee world. This Omakase menu lies at the heart of Substance Cafe's ethos – a celebration of the finest coffees, brewed to highlight the unique terroir of the farm where it was grown. The experience is akin to a tasting menu at Noma, where every course challenges your preconceptions about coffee. While the price of an espresso can reach $20, it's a small investment to partake in an experience that is among the best in the world. During my trip, I went to Substance Cafe three times, pulled in by the allure of its exceptional espresso offerings. Although Joachim is equally renowned for his V60 pourover coffee, I focused on espresso - a complex art form I'd never dare to attempt at home. Furthermore, it's a rarity to find cafes that can expertly pull shots from specialty light-roasted beans. Within the walls of Substance Cafe, I experienced the pinnacle of espresso – a Geisha from Finca Deborah in Panama. According to Joachim, this remarkable coffee is the only one he has ever bestowed a perfect 10 out of 10 rating for flavor. Each sip was akin to a kaleidoscopic journey, with a dynamic array of tastes unfurling on my palate – from vibrant orange to delicate jasmine. As the coffee cooled, its floral notes gracefully emerged, adding yet another layer of complexity to this unforgettable espresso experience. In an age where remote work and digital interactions dominate our lives, Substance Cafe serves as a refreshing reminder of the power and beauty of purpose-built physical spaces that foster engagement and connection. The cafe itself is a living testament to the artisan's relentless quest for excellence and the enriching experience it offers to its patrons. I can't help but dwell on the role the Internet plays in Substance Cafe. It enables Joachim to find and connect with niche community of coffee aficionados who share his values and devotion to the craft. These customers are willing to spend $20 on an espresso shot, visit the cafe only on weekday afternoons, and dedicate an hour to appreciating a cup of coffee. In this sense, the story of Substance Cafe serves as a testament to the positive effects of technology, enabling passionate individuals to create unique and meaningful work that resonates with a global audience. Ultimately, Substance Cafe exemplifies the remarkable potential that lies at the intersection of passion, craftsmanship, and technology. It serves as a beacon of inspiration for creators everywhere, reminding us that in a world that often moves too fast, there is still room for those who dare to slow down and strive for perfection in their craft.
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OpenAI, the path for OpenAI-powered startups, and the AI hype cycle

The rise of OpenAI has kicked off a new trend in technology: artificial intelligence startups. New AI startups launch every few hours, powered by OpenAI's different services for image generation, text generation, and even code generation. With Microsoft's recent $10 billion investment into OpenAI, we are at the precipice of a new hype cycle in technology. The market has greeted Artificial Intelligence with skepticism after recent trends like VR and Crypto haven't yet delivered on their world-changing promises. Market skepticism centers on one truth: Most new AI startups have developed no proprietary AI and rely entirely on OpenAI for basic functionality. I've spent the last six months helping startups develop AI products through The Contraption Company, including in-house AI systems and OpenAI-powered apps. Through this work, I've formed three perspectives that run counter to the market: 1. Better AI systems may soon overshadow OpenAI 2. OpenAI usage can lead to proprietary intellectual property 3. The AI hype cycle is substantive OpenAI alternatives - but you can't use them yet Machine learning (aka "AI") isn't new, and people interact with mature versions every day - from ads to newsfeeds to product recommendations. Many machine learning experts down the accomplishments of OpenAI because its systems seem like a commodity. Their sentiment appears to be, "with enough data and computers, it's possible to rebuild this." The way OpenAI's systems are not exceptionally secret - many of the techniques are public. Their founding team had the pedigree to raise enough money for a moonshot idea. And, OpenAI had the risk tolerance for being the first in the market to give external developers broad access to their technologies. Having enough data and computation to run AI is a barrier to startup AI companies. At the core is a chicken and egg problem: startups need data to train AI, and new companies typically need more data. These challenges have siloed cutting-edge machine learning to large corporations with enough data and talent to develop useful models. Google has long led the AI space - they have had substantive AI efforts for over a decade and routinely leverage AI in everything ranging from language translation to data center cooling. Google has a ChatGPT-style tool internally called LaMDA, but has avoided publishing it externally because of the brand risk an AI platform creates. No big tech company wants to repeat Microsoft's 2016 AI launch, which quickly ended with the front-page headline "Twitter taught Microsoft's AI chatbot to be a racist asshole in less than a day." AI acts in unexpected ways, and mature businesses prefer to avoid risk. However, OpenAI's traction seems to have compelled Google to rethink its AI services strategy. Google's forthcoming Bard announcement will likely be a competitor to OpenAI. The relative utility of ChatGPT versus Bard may be close initially. But Google has one significant advantage over OpenAI: hardware. Google's AI teams have been custom-building AI-optimized computer chips for their data centers since 2015. Proprietary Tensor Processor Units give Google a massive cost advantage over OpenAI that may help them scale and differentiate.  Google's forthcoming AI product should become a viable competitor to OpenAI and may even out-compete it. Over time, additional OpenAI competitors may emerge from companies with massive, propriety data sets - including Amazon and Facebook. OpenAI has a first-mover advantage, but its traction will drive competitors to enter the AI market. Generative AI solves a startup chicken-and-egg problem Machine learning is a broad field covering many different types of problems. AI services already exist - such as Amazon's Recommender service or Google's Vision AI service. OpenAI's success is primarily due to its strategic focus on one machine learning area: generative models. Generative AI models can take a few words as inputs and synthesize high-fidelity outputs such as photos, essays, or functional code. Creating an answer with limited input data short-circuits the chicken-and-egg problem facing startups - thus enabling a new generation of startups to bootstrap AI products without proprietary data. Generative models have a problem: accuracy. OpenAI models confidently return an incorrect answer, and OpenAI doesn't include confidence intervals signaling whether it thinks its response is adequate. OpenAI's models do a mediocre job of solving a broad set of problems. Its accuracy is typically high enough for initial prototypes with controlled inputs. But, as customer usage grows and inputs become less predictable - startups will usually see accuracy decline over time. Measuring accuracy is an essential step for most startups - this is why you'll see thumbs-up/thumbs-down buttons in most AI apps so that users can label the response as good or bad. Over time, startups build proprietary techniques to make OpenAI work better for them - often consisting of prompt engineering, vector embeddings, model tuning, and a collection of heuristics. This intellectual property typically helps startups scale their AI from a proof-of-concept to a stable product with customers. Customers flagging incorrect answers form a feedback loop that drives accuracy measurements and improvements over time. As usage grows, companies will start to see speed become the limiting tradeoff in model accuracy. Every query to OpenAI can take a couple of seconds to process, and that bottleneck impacts the customer experience. Overcoming the accuracy/speed tradeoff typically leads to replacing OpenAI with proprietary AI systems. As an application stores a log of customer queries, AI responses, and the customer labels of "good"/"bad" on every answer, this data can form the basis for a reinforcement learning AI model. Entirely replacing OpenAI may be a long process. Still, in-house AI systems have the advantage of being faster and more tractable in terms of accuracy.  Most OpenAI-powered startups will not get to the point of training their models. But, before OpenAI, training a custom model was the only option for most startups. Starting with AI helps startups get a prototype sooner and bootstrap the data ecosystem toward the same custom models - but in less time.  Over time, startups that train custom models may even publish an API - competing directly with OpenAI for a particular application. Over time, a library of more specialized OpenAI alternatives will inevitably become available.  How the AI hype cycle could play out To recap, this is my prediction for the trajectory of most AI startups: 1. Founders identify an attractive, niche problem that would benefit from an AI  2. Startup prototypes a functional product with OpenAI 3. Startup develops IP to improve accuracy as usage scales 4. A proprietary data set of inputs, outputs, and customer feedback becomes the basis for internal ML models 5. Start selling access to their own specialized AI models via an OpenAI-style API.\\ In the past, it took millions of dollars and years of work before a startup could sell its AI products to customers. Investors recognized significant market and implementation risks in funding these early AI startups, so investments were rare. Investor appetite for AI startups flipped last year because OpenAI's generative models enabled startups to prototype products and get customers without massive funding. A decrease in market and implementation risks explains why so many venture capitalists started investing in OpenAI-powered startups - kicking off the AI hype cycle. Historically, technology infrastructure has had high upfront costs - building an app, a data center, a FAB, or an AI model. Many technological innovations enable customers to trade these upfront costs for marginal costs. This model explains the success of low-code tools, AWS, TSMC, and now OpenAI. An AI startup following the above model can deliver a product to customers sooner and cheaper while eventually converging to the same proprietary AI they would have built without OpenAI. Along the way, OpenAI has unlocked an entire market of potential customers who never would have considered developing their own AI - such as small businesses.  Decreasing the upfront costs for artificial intelligence has been the core innovation of OpenAI. Founders can now prototype products without massive upfront investments, and this lower upfront cost means faster iteration and, ultimately, more innovation. Unlocking AI as a tool to solve customer problems is why this AI hype cycle has substance and will end in many massive, innovative companies.
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How to replace social media with a personal newsletter

Last week I shared how I'm building Postcard as a calmer alternative to social networks. I believe that personal newsletters will replace social networks like Twitter and Facebook as a dependable, personal way to stay in touch. People responded positively to the post, but asked how to get started. "Personal newsletter" is a great idea - but what should you write about? This post explains my philosophy and strategy for replacing social media with a monthly newsletter. A personal newsletter should sit somewhere between social media updates and a blog. Friendly, calm, and timely - but not too academic, formal, or permanent. Twitter and Facebook showed that people want a way to stay in touch with friends and family. But, in a tragedy of the commons, their newsfeeds rewarded people for being noisy and controversial. Over time, posts went from personal updates to stream-of-consciousness "hot takes" competing for likes. People flee these networks seeking a calm, personal alternative. Authors shouldn't think of their newsletter as a traditional blog.  The word "blog" has baggage - drawing to mind stodgy long-form essays that expound on abstract ideas and remain on the internet forever. That's why most people who start a blog never publish a first or second post - a blog is intimidating. Newsletters can be more temporary and lighthearted - closer to an email you'd send to friends. Here's the personal newsletter strategy that works for me: I publish an update on the first day of every month titled "What I'm up to." The newsletter has three sections, and I fill in each section with bullet points. I start drafting the next update as soon as I publish the previous one so that I can add thoughts throughout the month. On the first of the month, I finalize the post, email it to my list, and share the post on some social networks. Here are the three sections I include in every newsletter: • ✨ Highlights from last month • 🙌 Things to share • 📫 What I’m up to this month (Check out a recent example here). This newsletter strategy works because it has structure and a cadence. The structure of these sections makes both the reading and writing experiences easier - it's not a freeform essay. The monthly cadence means that I keep updates timely - I'm not waiting for some newsworthy "announcement" as an impetus for a post. Sharing the post on social networks lets me bridge my newsletter to people who still choose to use those networks. When creating your newsletter, start with this structure and cadence - then modify it to suit your personality. Success with a newsletter requires some recalibration of feedback. There's no "like" button and little data about what people like. Instead, pay attention to the improvements in your human connections. I feel fulfillment from the thoughtful replies people send to my newsletter. And, I enjoy it when people bring up something from a newsletter when we're chatting. If the idea of a personal newsletter appeals to you, try out Postcard. It's an app I'm building for hosting a personal newsletter and website. You can host it on your domain, and I'm working to make it the most powerful way to run a personal newsletter. 
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Why I built Postcard: A calmer alternative to social networks

During the pandemic, I deleted most of my social media accounts. While social networks started as a great way to stay in touch - the websites evolved into addictive entertainment platforms. But I still wanted to keep in touch with friends and family. So, I started a personal newsletter, and that's how Postcard was born. Facebook was the first and last great social network. That’s because the assumption was that every person was on Facebook, and it was a way to keep in contact that you knew in real life. Facebook was ubiquitous. As other websites like Twitter and Instagram grew, they never reached the scale of Facebook. These networks moved away from friends because they needed more content. This change happened when social networks began to make money on ads. Ads seek attention - driving social networks to move from helping people connect to capturing your attention for their advertisers. Social networks slowly evolved into social media. While social networks promoted knowing friends and staying in touch, social media promoted consuming content and following media personalities you didn’t know. Social media has become closer to a customized, 24/7 TV channel than a way to keep in touch. With the rise of AI, humans will no longer be creating content. Algorithms will study each user and generate custom content designed to addict that person, all in the pursuit of feeding them more ads. It sounds dystopian - but we’re not far from it. Social media is no longer about community - it’s about ad impressions. There’s good news, though. There's a calm way to stay in touch with everybody: Personal newsletters. Even the staunchest anti-social media advocates keep a newsletter. Cal Newport, the author of “Digital Minimalism,” eschews all social networks but has maintained a personal newsletter for over a decade. This is because newsletters go to your email inbox and skip any algorithmic boosts or manipulation that control what content you see. If you are ready to break the cycle on addictive social networks, try setting up a personal newsletter with Postcard. You can still participate in social networks by sharing posts on those sites. But you can build a newsletter over time that you own. 
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When are low-code prototypes useful? Evaluating startup market and implementation risks

Ask for advice on how to make a startup, and most people recommend starting with a low-code prototype.  There are categories of businesses where low-code prototypes can help you de-risk a new idea: • Blogging software (like Ghost did) • Marketplaces (like we did with Moonlight) • Delivery (like Savioke did) But, there are other categories where a prototype won't provide a meaningful signal: • AI / deep tech (like DALL·E) • Search engines (like Google) • Space travel (like Blue Origin) To decide which ideas benefit from low-code prototyping, you can apply the frameworks of execution risk and market risk. Execution risk: Can you develop the technology to make this work? For example, making a website in 2022 has low execution risk because many people possess the skills, and there are tools such as Squarespace or Webflow that simplify the process. Building a self-driving truck has high execution risk because nobody has achieved level 5 autonomy with any self-driving car. Market risk: Do people want this, and is there a big enough market? For example, there is a low market risk for an apartment finder in Manhattan because people are already actively seeking that product and are willing to pay for it. Conversely, building a new social network has high market risk - because its success means competing with Tiktok and Instagram for a person's finite attention. Comparing market risk with execution risk, we can categorize businesses as Applications, Moonshots, Copycats, or Hard Tech. Applications leverage known technologies to solve a problem in a new way. Many well-known consumer tech startups, such as marketplaces and ecommerce, fall into this category. Airbnb is a great example - the software for booking hotels existed, but nobody thought to apply that software to booking peer-to-peer. The success of Airbnb was limited by whether people wanted to sleep in somebody else's apartment, not whether they could build it. Moonshots invent new technologies to solve new problems. Bitcoin was a great example - Satoshi sought to develop a digital currency that nobody controlled. To do it, Satoshi had to invent blockchain technology. Bitcoin had both technology and market risks - not only had nobody created a decentralized currency before, but Satoshi did not know whether anybody would adopt Bitcoin. Copycats use existing technologies to address a known problem. These can still be lucrative businesses - most often when an existing business adds a new feature that its customers already want. For example, the success of Slack removed market risk from workplace chat products. So, when Microsoft built Microsoft Teams as a chat product and offered it to their existing customers, Microsoft Teams became more successful than Slack. Hard tech companies invent new technologies to solve complicated unsolved problems. For instance, Boom is building an airplane that flies twice as fast. Airlines already buy aircrafts and know customers would pay more money to arrive in half the time. Boom's success isn't limited by whether airlines want a faster airplane - it's determined by whether they can build a cost-effective aircraft that meets existing safety and reliability standards. So, when are low-code prototypes useful? Low-code prototypes work best for Applications - where there is low execution risk and high market risk. When your business doesn't require the invention of a brand-new technology to succeed, then execution risk is low. But, when you don't know whether people want it, the business carries significant market risk. Building a prototype and showing it to users can help you de-risk whether people want it.
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Advice for marketplace startups

Marketplace businesses connect buyers and sellers, and they typically make money by taking a cut of transactions. Some of today's largest companies are marketplaces, such as Amazon, Airbnb, and Uber. I spent three years building a marketplace for software engineering gigs called Moonlight. It took us about two years of experimentation to make something that people wanted, then another year of growth to get acquired. Those two years of product development became a crash course for me in how to build a marketplace as we constantly experimented with our business until something worked. For startup founders interested in starting a marketplace business, here is my distilled advice that I hope will save you two years of work. Focus on demand. Many founders approach marketplaces from the supply side - a group of people looking for work. But, be careful - the actual group that defines the marketplace is the buyer. Ask yourself: who wants to buy this product, and what problem do they want to be solved? Does the buyer even have the budget to purchase what I'm selling? What you sell is your product. That is where the market risk lies for your business. How you fulfill that product has operational risk but isn't typically where the "secret sauce" for a company lives. Uber didn't start their business by saying, "Wow, lots of drivers are sitting around with no work." Instead, they began with the ideal customer experience - on-demand rides. Over time, Uber grew the number of people working as professional drivers because they offered fair pay and stable work. Why a marketplace business model? For a marketplace to work, you need to deliver ongoing value to both buyers and suppliers, and you need to have the supply work on multiple projects. Often, many different business models could apply to the same problem. If your supply prefers full-time jobs over gigs, then maybe you should monetize in contingency agreements where you earn a recruiting placement fee. If companies wish to work directly with suppliers, then perhaps you should be selling a lead generation service like Craigslist. If companies want to outsource projects, then maybe you should build a full-service agency. Most marketplaces start as an agency where humans manage the entire process from end to end. Beginning as an agency proves that buyers want to buy your product and is an excellent way to get started. But, the difficult part is transitioning from a service business into a technology business. Some companies navigated this transition well, such as Airbnb, and others navigated it poorly, such as Gigster. Think about commoditization. Are you selling the individual skills of each worker on your marketplace, or can any person do any job on the marketplace? There are some significant implications here for how matching works. If hiring is such a "considered purchase" for the buyer - how can you automate it enough to get buyers to make a decision instead of taking weeks to interview tons of people? Airbnb successfully pushes customers to pick between drastically different listings at different prices. But, that same model doesn't work in every business. As a rule of thumb, every decision you ask buyers to make in a marketplace is an opportunity for them to drop off without making a purchase. Sidecar was an early ridesharing startup that competed with Lyft and Uber. But, Sidecar let drivers set their price - and it turned out that customers were not equipped with the information or patience to choose the exact driver they wanted every time. Uber's innovation of constant pricing meant that every driver cost the same. So, they could route the closest driver to you, and if there was a problem - they could reassign the driver without your approval. For your marketplace: does letting your customer pick the supplier and price improve or detract from the experience? Beware disintermediation. Agencies have recruiters that make sure that the demand and supply follow the rules. Once you become too big to manually handle every deal (and truly become a marketplace), then you need people to follow the rules still. If people don't follow the rules, then you spend a lot of money acquiring customers and suppliers who cut you out of the deal. The more a company thinks they've hired a particular person, instead of hiring your company - the more likely you will get disintermediated. Yes, you can write any rules into contracts about fees and needing to go through you. But, suing customers isn't a viable growth strategy. You can only get compounding growth if you can consistently grow the number of working suppliers. And, if you need humans to enforce the rules - then your business is an agency, not a marketplace. The home cleaning startup Homejoy failed because their customers had a stronger loyalty to a cleaner than the Homejoy brand. So, Homejoy would pay for ads to get customers, but after a first cleaning - the customer would typically rebook directly with the cleaner instead of the app. Managed by Q innovated on this cleaning model and managed to make it viable. They sold cleaning services to companies who were less likely to form loyalty to individual cleaners, and they made their cleaners full-time employees with benefits so that they could rely on the company for all of their income. The frequency of purchase will determine whether your marketplace can experience exponential growth in the number of active customers. So, ask yourself - why would a customer use this marketplace the second, third, or hundredth time? Also, can your suppliers rely on your marketplace for stable income? Your pricing is too low. At a 5% take rate, you need about 20 full-time working placements to pay one internal employee. That's crazy. I always recommend that marketplaces aim for closer to a 50% fee. With a 50% fee, you only need one full-time working placement to support one internal employee. High prices might seem like a problem - but it's a competitive advantage. If you differentiate on nothing but the price, then your business is a commodity and can get replaced. The amount of money you make dictates how much money you can spend to acquire new customers - which drives faster growth. Low fees can hurt your ability to pursue enterprise customers, too. Big companies will want you to invoice them with 90-day payment terms, but suppliers don't want to wait three months for payment - which means you're factoring invoices and taking on the risk of non-payment. High margins help make both of these risks more palatable. Consider TAM. To raise VC, you need a path to $100m net revenue per year. (Probably higher because your revenue isn't recurring). How many suppliers could work on your site, and how much money could you earn? Is that even possible? And, why would that labor continue working through you instead of leaving the platform to go work for somebody else? Small decisions in marketplace businesses have significant impacts on the viability of the business. So, be strategic about every detail, and focus on creating the ideal customer experience above all else.
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Moonlight's pitch deck

In 2017, I co-founded Moonlight as a professional community for software developers with Emma Lawler. We were both leaving San Francisco, and wanted to make it easier for all developers to find specialized, remote work options. We envisioned a Silicon Valley diaspora that would permanently transform knowledge work into a distributed, work-from-home career. Starting with a no-code prototype, we slowly built a web application and iterated on different parts of the business model. In 2019 we raised a pre-seed round of funding. Fathom Capital and Goldcrest Capital co-led the round, and other participants included Haystack, Jeremy Yap, Quinn Slack, Luke Kanies, Hampus Jakobsson, and Aghi Marietti. In 2020, PullRequest acquired Moonlight. Their developer marketplace, funded primarily by Google, continues to operate and grow the product today. Here, I'm publishing the pitch deck that we used to raise investment money for Moonlight in 2019. I want to share the vision we had pitched for the future of knowledge work, and I want to demystify the process of fundraising for other founders. Below each slide, I've included prose typical of how I would present the slide. As a reminder, this presentation is being shared for informational purposes only. Moonlight is not raising money, and no offer of investment is being made or solicited. Moonlight's pitch Today I'd like to talk to you about Moonlight, which is a professional community of software developers. [Slide 2] Our mission is to help the world work together to build the future. [Slide 3] Emma and I co-founded Moonlight, and bring extensive experience in building both digital products and startups. Emma worked as a product designer at Fitstar, which was acquired by Fitbit shortly before its IPO. She went to school at CU Boulder. I was the founder of the workforce management startup Staffjoy, a Fellow at Y Combinator for that company, and an engineer at OpenDNS, during which it was acquired by Cisco. I am a graduate of Washington University in St. Louis' School of Engineering. Emma and I both have technical backgrounds in product design and engineering. Emma designs the product and I build it. [Slide 4] We started Moonlight to solve our own problem. Two years ago, we both left San Francisco to travel full-time, but we wanted to keep working with great companies that valued our specialized skills. We were fortunate to have great networks from living in San Francisco, which gives us access to high-quality contract work. But, most people in the world do not have direct access to growing tech companies. So, we built Moonlight to address this problem. We left the Bay Area, and built Moonlight from Airbnbs on five different continents over two years. Around the world, we met software engineers and companies to understand how the technology industry is changing, and became deeply involved with a variety of international communities ranging from Startup School by Y Combinator to flying to India with the US State Department. [Slide 5] As we traveled, we realized that remote workers have an unmet hierarchy of needs. The first thing you need to become a remote worker is a remote job. But, we have found that workers' needs extend far beyond that. Most remote workers enter as contract workers, but desire stability in terms of salary, insurance, and vacation - meaning, they want employment. Once these workers have stability, they seek to address loneliness through a sense of community. Next, workers often report that it's hard to go from junior to senior in their careers outside of an office. We think this is because remote teams have fewer spontaneous and unstructured interactions, which makes it harder to learn on-the-job from teammates. We think there's an opportunity to help here with things like training and certifications. Ultimately, people want to work on something that they really care about, which the top of the pyramid. [Slide 6] We built Moonlight to solve the needs of remote knowledge workers. We started with jobs - by helping people find project-based work. We thought that project-based work might be the future, but found that most freelancers return to a full-time role within two years. In fact, about one in seven contract jobs on Moonlight ends up converting to full-time employment. In the future, we plan on moving up the pyramid with community, advancement, and recognition. Ultimately, we want to help people find the right job for their interests. [Slide 7] The problem we're addressing is that companies have trouble hiring software engineers. Stripe's Developer Coefficient report last year found that "access to developers is a bigger constraint than access to capital." These potential customers have money that they're trying to spend, without success. As software "eats the world" and work becomes distributed, we foresee 10x to 100x as many applicants for every open role, making the hiring process even harder [Slide 8] Our view is that software work is becoming globalized. Remote collaboration is becoming easier with tools such as Slack and Zoom. The demand for developers is rising as every company from Boeing to JP Morgan to John Deere becomes a software company. And, programs such as Lambda School and General Assembly are creating a new wave of developers entering the market. [Slide 9] We believe that distributed work is the future, and that it solves the problem of companies being unable to hire developers. Stripe addressed their own perceived lack of developers by officially opening a remote office. And, there's a trend of more and more companies going distributed, ranging from GitHub to Product Hunt to Stripe to WordPress. [Slide 10] Our view is that today's traditional, localized hiring networks don't scale to a new global labor market. Hired had an early start, and launches city-by-city in limited markets. They are actively trying to move away from contingency-based pricing to subscription pricing, and they have high overhead due to their sales model. LinkedIn uses an antiquated approach to matching people to jobs. It focuses on a network model based on who you know, so that you can find jobs through your second and third-degree connections. But, in a global hiring market, hiring managers may have no connections with the best possible worker for their team. TripleByte rejects most candidates and has a low acceptance rate of offers. Their success is predicated on a supply-constrained market. We believe that the rise of coding schools and remote work threatens this supply-first model. [Slide 11] Our name, Moonlight, comes a latent behavior in the software community. Developers already work on side projects and side gigs in their free time, and they're often doing it from home - not an office. We focus on three different personas of developers who moonlight. One is the "Explorer." These tend to be people that are specialized and senior, and are looking to adopt technologies early. They moonlight for passion. Think of developers working on Bitcoin - before it became an employable skill, it was a passion project. Second, there are the "Independents." These are people that are experienced and looking for advancement. They moonlight to trial new teams. They rarely enter the open job market because they're highly employable and have a network. They use trial projects to evaluate potential teams for long-term work. Think of them as people who will help their friends with startups on nights and weekends, and then transition to full-time once the company matures. Third, there are the "Beginners." These tend to be more generalist people who are just finishing a coding school. They seek stability, and moonlight prove their ability. We observe many coding schools advocating for contract-to-hire so that candidates can get into companies and stand out from the crowd. [Slide 12] Remote work is changing the market. This broader talent pool means that people need to be matched by specialities and experience, instead of just location. Every developer is going to be a candidate for every job in the world. Without in-person "whiteboard interviews", we strongly believe that trial-based hiring will become the norm, which we call "moonlighting." Candidates will contract with a company for anywhere from a few days to a few months, and companies will evaluate them based on their actual performance. With no office, the provider of career advancement resources and mentorship opportunities will transition from the company to a third-party. This is where Moonlight plans to expand in the future. [Slide 13] Today, Moonlight is a SaaS business. We provide job matching, managed contract-to-hire (including payments), candidate alerts for companies, and a weekly newsletter. In the future, we plan to work more on education for distributed team best practices, career support for remote workers, and community such as online and local groups for developers. [Slide 14] Under the hood, Moonlight is a data-driven software company. We track over 500 different engineering skills ranging from Kubernetes to Python. We've tracked over four million data points of how people are using the product, and we've identified over 50,000 potential users on job boards. On top of that data, we've built machine learning models such as associations of different skills. So, we can tell that a developer who lists React as a skill also knows Javascript. Based on these skills and data points, we are pursuing a long-tail SEO strategy with over 10,000 auto-generated landing pages. So, if you search for React developers or developers in Cleveland, we have pages for that. Finally, given a company's website, these tools all work together to be able to identify the right candidates for a particular hiring manager. The result is that we have about one-in-four cold email response rate, relevant job matches and, ultimately, we're closing hires. [Slide 15] Today 1,700 developers who want flexible work have joined and been approved for Moonlight's network. They apply to Moonlight by setting up a free profile, configure payments through Stripe, and we manually screen each applicant. Most remote work websites focus on outsourcing. We don't. About two-thirds of Moonlight's developers are located in North America. Developers on Moonlight have an average of nine years of professional experience, and on average earn over $100 an hour. Almost everybody is seeking contract work at any given time, while a third of candidates are open to full-time work opportunities. Our strategy is to engage passive candidates with contract work. Then, when they begin looking for a full-time role, we can immediately identify that intent and match them with the ideal roles. [Slide 16] Developers find Moonlight in three different ways. First, we have identified over 40,000 people looking for work, and when we email them 15% sign up. We plan to expand partnerships in the future. Moonlight is currently a Stripe Verified Partner. We are actively in pilots with some coding schools. And, we are building out a certifications module so that we can co-market expert communities with companies who need "sales engineering"-style support. About a fifth of our new customers come from referrals. We hold events in most major markets and we really do a great job of attracting passive candidates. [Slide 17] Our revenue is growing 16% week-over-week. This graph shows our path to $7k MRR, and the live revenue just passed $10k MRR this morning. [Slide 18] The $300 monthly cost per seat is designed for bottoms-up distribution, and our goal is to grow land-and-expand revenue. Customers range from unicorns like CloudFlare to hours-old startups.[Slide 19] The total attainable market is huge. With half a million software engineering managers in the United States each spending $300 per seat, we estimate a $2 billion per year market opportunity. [Slide 20] Finally, long-term we are building a remote-first community. Our strategy is to first scale the SaaS business to fund growth. Second, we will grow community and network to retain developers throughout their career. And third, we will develop tooling to facilitate remote work. We already have built active chat channels integrated with Slack that have thousands of participants, and we have organized meetups around the world. We can see the future of Moonlight as a verticalized professional community - something like a mix of LinkedIn, GitHub, and Quora. [Slide 21] Today we're raising our first round of funding to double-down on growth. Our goal is to hire a front-end engineer and operations coordinator, aggressively go to market with per-seat membership, drive network effects to fuel growth, and create new features to increase engagement. [Slide 22] Our mission is to help the world work together. We see Moonlight as having co-working benefits without the office, helping companies find and retain remote workers throughout their careers, offer career advancement opportunities for workers, and ultimately to build tools that empower distributed teams. [Slide 23] That is Moonlight - the product is live at Appendix We structured our appendix with a Q&A card and metrics graphs. For later rounds of funding, it is typical to have an entire metrics deck. But, for earlier rounds of funding, you basically want to be prepared to answer common questions that investors may have.  [Slide 24] [Slide 25] [Slide 26] [Slide 27] [Slide 28] [Slide 29] [Slide 30] Send me your deck! If you have any questions, please feel free to email me at If you're working on a pitch deck, I'd love to provide feedback! Please send it to me. If you enjoyed this article, you may also enjoy my 2017 article Staffjoy's Pitch Decks That Raised $1.7m.
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Buyers define marketplaces

A marketplace business connects buyers and sellers in exchange for a cut of the revenue. Some of today's biggest companies, such as Amazon, Uber, and Airbnb, are marketplaces. Yes, these companies changed how millions of people work and earn money. However, the core innovation of these businesses is how they use technology to solve customer problems. In 2017, I struggled to hire full-time software developers but found many that were willing to work nights and weekends for extra money. So, I started Moonlight as a marketplace to connect developers with weekend work. Thousands of software engineers quickly signed up, but hiring managers showed less eagerness to join. It turned out that you cannot kickstart a marketplace with sellers alone. I learned a valuable lesson: buyers define the market. Startups seek to build something that people want. Finding this product/market fit once is hard - but in a marketplace, you must do it twice. Buyers and sellers on a marketplace have separate wants and needs, each with a distinct set of quirks. The critical insight is that these challenges are not equal - because you are dealing with money. The demand from buyers will inevitably attract a supply of sellers. There are more professional drivers today than there were ten years ago because Uber and Lyft expanded the market. When there are new ways to earn money, labor will follow. Fair pay gets sellers on a marketplace. Aggregating sellers does not make a marketplace. Marketplaces earn their cut of revenue by being able to find buyers better than the suppliers alone. Personal shoppers existed long before Instacart, but it was hard for them to find customers. If sellers can find enough customers on their own, they don't need the platform - leading to disintermediation. Disintermediation killed marketplaces such as Homejoy and Handy. Marketplaces justify their existence by solving a buyer's problems in a novel way. They identify some business transaction where the buyer is under-served by an existing seller, and where technology can transform the relationship. People couldn't reliably get transported by taxis and were happy to spend more money on Uber. Hotels were expensive and conventional, so travelers spent money on Airbnb instead. Searching for a particular product at stores was hard, so Amazon built a product search engine. Technological innovations such as the internet and smartphones meant that these businesses could not have existed sooner. Marketplaces must create demand, then that demand attracts sellers. After the initial influx of developers to Moonlight, we spent most of the next few years changing the product and pricing to attract hiring managers as buyers. The business grew and was ultimately acquired earlier this year. We thought that Moonlight found a new way for developers to work, but its real innovation was changing how software teams hired.
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