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44

How to raise investment

from Tom Blomfield [alt+shift+b] in startups

Update: I am no longer personally angel investing - I’m focussed on other things at the moment. Please don’t ask me to invest in your company! 🙂 I started angel-investing in 2021, and a large proportion of the companies I put money into asked for help raising an investment round. So I wrote up my standard advice as a guide here. This guide is based on my experience raising about $400m at Monzo over the last 6 years and investing in about 75 seed deals as an angel in 2021. It will be most relevant for founders who are raising a Seed or Series A round, with some amount of product development and early customer traction. I believe the following advice is mostly correct as of September 2021. The market is changing so quickly that it may not stay correct for long. I plan to update it periodically. General Approach First, a couple of warnings. If you build a strong business, fundraising will normally be easy. If you’re too focussed on fundraising, you may never actually build a strong business. It’s very easy to get sucked into the competition of raising at the highest valuation of all your peers. This may feel like winning, but it is not. Seed-stage valuations don’t correlate well with success. Success to me means building a sustainable, profitable company that makes something people love. Investment is just a tool to help you do this. Airbnb raised its first round out of YC at a $1.8m valuation. They’ve done pretty well. An extra 3-5% dilution isn’t going to matter if you build a very successful company. Conventional wisdom says you should aim to raise enough money to fund your business for the next 18 to 24 months, or until you hit a major milestone that’s going to create a step-change in the valuation of the business. If that next milestone is “reaching breakeven”, you will have a dramatically easier time fundraising in future - you can decide whether to raise more to grow faster, or run the business for profit. You have all the power. I have always spent...
11th Sep 2021

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Vibecoding a production app

TL;DR I built and launched a recipe app with about 20 hours of work - recipeninja.ai Background: I’m a startup founder turned investor. I taught myself (bad) PHP in 2000, and picked up Ruby on Rails in 2011. I’d guess 2015 was the last time I wrote a line of Ruby professionally. I’ve built small side projects over the years, but nothing with any significant usage. So it’s fair to say I’m a little rusty, and I never really bothered to learn front end code or design. In my day job at Y Combinator, I’m around founders who are building amazing stuff with AI every day and I kept hearing about the advances in tools like Lovable, Cursor and Windsurf. I love building stuff and I’ve always got a list of little apps I want to build if I had more free time time. About a month ago, I started playing with Lovable to build a word game based on Articulate (it’s similar to Heads Up or Taboo). I got a working version, but I quickly ran into limitations - I found it very complicated to add a supabase backend, and it kept re-writing large parts of my app logic when I only wanted to make cosmetic changes. It felt like a toy - not ready to build real applications yet. But I kept hearing great things about tools like Windsurf. A couple of weeks ago, I looked again at my list of app ideas to build and saw “Recipe App”. I’ve wanted to build a hands-free recipe app for years. I love to cook, but the problem with most recipe websites is that they’re optimized for SEO, not for humans. So you have pages and pages of descriptive crap to scroll through before you actually get to the recipe. I’ve used the recipe app Paprika to store my recipes in one place, but honestly it feels like it was built in 2009. The UI isn’t great for actually cooking. My hands are covered in food and I don’t really want to touch my phone or computer when I’m following a recipe. So I set out to build what would become RecipeNinja.ai For this project, I decided to use Windsurf. I wanted a Rails 8 API backend and React front-end app and Windsurf set this up for me in no time. Setting up homebrew on a new laptop, installing npm and making sure I’m on the right version of Ruby is always a pain. Windsurf did this for me step-by-step. I needed to set up SSH keys so I could push to GitHub and Heroku. Windsurf did this for me as well, in about 20% of the time it would have taken me to Google all of the relevant commands. I was impressed that it started using the Rails conventions straight out of the box. For database migrations, it used the Rails command-line tool, which then generated the correct file names and used all the correct Rails conventions. I didn’t prompt this specifically - it just knew how to do it. It one-shotted pretty complex changes across the React front end and Rails backend to work seamlessly together. To start with, the main piece of functionality was to generate a complete step-by-step recipe from a simple input (“Lasagne”), generate an image of the finished dish, and then allow the user to progress through the recipe step-by-step with voice narration of each step. I used OpenAI for the LLM and ElevenLabs for voice. “Grandpa Spuds Oxley” gave it a friendly southern accent. Recipe summary: And the recipe step-by-step view: I was pretty astonished that Windsurf managed to integrate both the OpenAI and Elevenlabs APIs without me doing very much at all. After we had a couple of problems with the open AI Ruby library, it quickly fell back to a raw ruby HTTP client implementation, but I honestly didn’t care. As long as it worked, I didn’t really mind if it used 20 lines of code or two lines of code. And Windsurf was pretty good about enforcing reasonable security practices. I wanted to call Elevenlabs directly from the front end while I was still prototyping stuff, and Windsurf objected very strongly, telling me that I was risking exposing my private API credentials to the Internet. I promised I’d fix it before I deployed to production and it finally acquiesced. I decided I wanted to add “Advanced Import” functionality where you could take a picture of a recipe (this could be a handwritten note or a picture from a favourite a recipe book) and RecipeNinja would import the recipe. This took a handful of minutes. Pretty quickly, a pattern emerged; I would prompt for a feature. It would read relevant files and make changes for two or three minutes, and then I would test the backend and front end together. I could quickly see from the JavaScript console or the Rails logs if there was an error, and I would just copy paste this error straight back into Windsurf with little or no explanation. 80% of the time, Windsurf would correct the mistake and the site would work. Pretty quickly, I didn’t even look at the code it generated at all. I just accepted all changes and then checked if it worked in the front end. After a couple of hours of work on the recipe generation, I decided to add the concept of “Users” and include Google Auth as a login option. This would require extensive changes across the front end and backend - a database migration, a new model, new controller and entirely new UI. Windsurf one-shotted the code. It didn’t actually work straight away because I had to configure Google Auth to add `localhost` as a valid origin domain, but Windsurf talked me through the changes I needed to make on the Google Auth website. I took a screenshot of the Google Auth config page and pasted it back into Windsurf and it caught an error I had made. I could login to my app immediately after I made this config change. Pretty mindblowing. You can now see who’s created each recipe, keep a list of your own recipes, and toggle each recipe to public or private visibility. When I needed to set up Heroku to host my app online, Windsurf generated a bunch of terminal commands to configure my Heroku apps correctly. It went slightly off track at one point because it was using old Heroku APIs, so I pointed it to the Heroku docs page and it fixed it up correctly. I always dreaded adding custom domains to my projects - I hate dealing with Registrars and configuring DNS to point at the right nameservers. But Windsurf told me how to configure my GoDaddy domain name DNS to work with Heroku, telling me exactly what buttons to press and what values to paste into the DNS config page. I pointed it at the Heroku docs again and Windsurf used the Heroku command line tool to add the “Custom Domain” add-ons I needed and fetch the right Heroku nameservers. I took a screenshot of the GoDaddy DNS settings and it confirmed it was right. I can see very soon that tools like Cursor & Windsurf will integrate something like Browser Use so that an AI agent will do all this browser-based configuration work with zero user input. I’m also impressed that Windsurf will sometimes start up a Rails server and use curl commands to check that an API is working correctly, or start my React project and load up a web preview and check the front end works. This functionality didn’t always seem to work consistently, and so I fell back to testing it manually myself most of the time. When I was happy with the code, it wrote git commits for me and pushed code to Heroku from the in-built command line terminal. Pretty cool! I do have a few niggles still. Sometimes it’s a little over-eager - it will make more changes than I want, without checking with me that I’m happy or the code works. For example, it might try to commit code and deploy to production, and I need to press “Stop” and actually test the app myself. When I asked it to add analytics, it went overboard and added 100 different analytics events in pretty insignificant places. When it got trigger-happy like this, I reverted the changes and gave it more precise commands to follow one by one. The one thing I haven’t got working yet is automated testing that’s executed by the agent before it decides a task is complete; there’s probably a way to do it with custom rules (I have spent zero time investigating this). It feels like I should be able to have an integration test suite that is run automatically after every code change, and then any test failures should be rectified automatically by the AI before it says it’s finished. Also, the AI should be able to tail my Rails logs to look for errors. It should spot things like database queries and automatically optimize my Active Record queries to make my app perform better. At the moment I’m copy-pasting in excerpts of the Rails logs, and then Windsurf quickly figures out that I’ve got an N+1 query problem and fixes it. Pretty cool. Refactoring is also kind of painful. I’ve ended up with several files that are 700-900 lines long and contain duplicate functionality. For example, list recipes by tag and list recipes by user are basically the same. Recipes by user: This should really be identical to list recipes by tag, but Windsurf has implemented them separately. Recipes by tag: If I ask Windsurf to refactor these two pages, it randomly changes stuff like renaming analytics events, rewriting user-facing alerts, and changing random little UX stuff, when I really want to keep the functionality exactly the same and only move duplicate code into shared modules. Instead, to successfully refactor, I had to ask Windsurf to list out ideas for refactoring, then prompt it specifically to refactor these things one by one, touching nothing else. That worked a little better, but it still wasn’t perfect Sometimes, adding minor functionality to the Rails API will often change the entire API response, rather just adding a couple of fields. Eg It will occasionally change Index Recipes to nest responses in an object { “recipes”: [ ] }, versus just returning an array, which breaks the frontend. And then another minor change will revert it. This is where adding tests to identify and prevent these kinds of API changes would be really useful. When I ask Windsurf to fix these API changes, it will instead change the front end to accept the new API json format and also leave the old implementation in for “backwards compatibility”. This ends up with a tangled mess of code that isn’t really necessary. But I’m vibecoding so I didn’t bother to fix it. Then there was some changes that just didn’t work at all. Trying to implement Posthog analytics in the front end seemed to break my entire app multiple times. I tried to add user voice commands (“Go to the next step”), but this conflicted with the eleven labs voice recordings. Having really good git discipline makes vibe coding much easier and less stressful. If something doesn’t work after 10 minutes, I can just git reset head –hard. I’ve not lost very much time, and it frees me up to try more ambitious prompts to see what the AI can do. Less technical users who aren’t familiar with git have lost months of work when the AI goes off on a vision quest and the inbuilt revert functionality doesn’t work properly. It seems like adding more native support for version control could be a massive win for these AI coding tools. Another complaint I’ve heard is that the AI coding tools don’t write “production” code that can scale. So I decided to put this to the test by asking Windsurf for some tips on how to make the application more performant. It identified I was downloading 3 MB image files for each recipe, and suggested a Rails feature for adding lower resolution image variants automatically. Two minutes later, I had thumbnail and midsize variants that decrease the loading time of each page by 80%. Similarly, it identified inefficient N+1 active record queries and rewrote them to be more efficient. There are a ton more performance features that come built into Rails - caching would be the next thing I’d probably add if usage really ballooned. Before going to production, I kept my promise to move my Elevenlabs API keys to the backend. Almost as an afterthought, I asked asked Windsurf to cache the voice responses so that I’d only make an Elevenlabs API call once for each recipe step; after that, the audio file was stored in S3 using Rails ActiveStorage and served without costing me more credits. Two minutes later, it was done. Awesome. At the end of a vibecoding session, I’d write a list of 10 or 15 new ideas for functionality that I wanted to add the next time I came back to the project. In the past, these lists would’ve built up over time and never gotten done. Each task might’ve taken me five minutes to an hour to complete manually. With Windsurf, I was astonished how quickly I could work through these lists. Changes took one or two minutes each, and within 30 minutes I’d completed my entire to do list from the day before. It was astonishing how productive I felt. I can create the features faster than I can come up with ideas. Before launching, I wanted to improve the design, so I took a quick look at a couple of recipe sites. They were much more visual than my site, and so I simply told Windsurf to make my design more visual, emphasizing photos of food. Its first try was great. I showed it to a couple of friends and they suggested I should add recipe categories - “Thai” or “Mexican” or “Pizza” for example. They showed me the DoorDash app, so I took a screenshot of it and pasted it into Windsurf. My prompt was “Give me a carousel of food icons that look like this”. Again, this worked in one shot. I think my version actually looks better than Doordash 🤷‍♂️ Doordash: My carousel: I also saw I was getting a console error from missing Favicon. I always struggle to make Favicon for previous sites because I could never figure out where they were supposed to go or what file format they needed. I got OpenAI to generate me a little recipe ninja icon with a transparent background and I saved it into my project directory. I asked Windsurf what file format I need and it listed out nine different sizes and file formats. Seems annoying. I wondered if Windsurf could just do it all for me. It quickly wrote a series of Bash commands to create a temporary folder, resize the image and create the nine variants I needed. It put them into the right directory and then cleaned up the temporary directory. I laughed in amazement. I’ve never been good at bash scripting and I didn’t know if it was even possible to do what I was asking via the command line. I guess it is possible. After launching and posting on Twitter, a few hundred users visited the site and generated about 1000 recipes. I was pretty happy! Unfortunately, the next day I woke up and saw that I had a $700 OpenAI bill. Someone had been abusing the site and costing me a lot of OpenAI credits by creating a single recipe over and over again - “Pasta with Shallots and Pineapple”. They did this 12,000 times. Obviously, I had not put any rate limiting in. Still, I was determined not to write any code. I explained the problem and asked Windsurf to come up with solutions. Seconds later, I had 15 pretty good suggestions. I implemented several (but not all) of the ideas in about 10 minutes and the abuse stopped dead in its tracks. I won’t tell you which ones I chose in case Mr Shallots and Pineapple is reading. The app’s security is not perfect, but I’m pretty happy with it for the scale I’m at. If I continue to grow and get more abuse, I’ll implement more robust measures. Overall, I am astonished how productive Windsurf has made me in the last two weeks. I’m not a good designer or frontend developer, and I’m a very rusty rails dev. I got this project into production 5 to 10 times faster than it would’ve taken me manually, and the level of polish on the front end is much higher than I could’ve achieved on my own.  Over and over again, I would ask for a change and be astonished at the speed and quality with which Windsurf implemented it. I just sat laughing as the computer wrote code. The next thing I want to change is making the recipe generation process much more immediate and responsive. Right now, it takes about 20 seconds to generate a recipe and for a new user it feels like maybe the app just isn’t doing anything. Instead, I’m experimenting with using Websockets to show a streaming response as the recipe is created. This gives the user immediate feedback that something is happening. It would also make editing the recipe really fun - you could ask it to “add nuts” to the recipe, and see as the recipe dynamically updates 2-3 seconds later. You could also say “Increase the quantities to cook for 8 people” or “Change from imperial to metric measurements”. I have a basic implementation working, but there are still some rough edges. I might actually go and read the code this time to figure out what it’s doing! I also want to add a full voice agent interface so that you don’t have to touch the screen at all. Halfway through cooking a recipe, you might ask “I don’t have cilantro - what could I use instead?” or say “Set a timer for 30 minutes”. That would be my dream recipe app! Tools like Windsurf or Cursor aren’t yet as useful for non-technical users - they’re extremely powerful and there are still too many ways to blow your own face off. I have a fairly good idea of the architecture that I want Windsurf to implement, and I could quickly spot when it was going off track or choosing a solution that was inappropriately complicated for the feature I was building. At the moment, a technical background is a massive advantage for using Windsurf. As a rusty developer, it made me feel like I had superpowers. But I believe within a couple of months, when things like log tailing and automated testing and native version control get implemented, it will be an extremely powerful tool for even non-technical people to write production-quality apps. The AI will be able to make complex changes and then verify those changes are actually working. At the moment, it feels like it’s making a best guess at what will work and then leaving the user to test it. Implementing better feedback loops will enable a truly agentic, recursive, self-healing development flow. It doesn’t feel like it needs any breakthrough in technology to enable this. It’s just about adding a few tool calls to the existing LLMs. My mind races as I try to think through the implications for professional software developers. Meanwhile, the LLMs aren’t going to sit still. They’re getting better at a frightening rate. I spoke to several very capable software engineers who are Y Combinator founders in the last week. About a quarter of them told me that 95% of their code is written by AI. In six or twelve months, I just don’t think software engineering is going exist in the same way as it does today. The cost of creating high-quality, custom software is quickly trending towards zero. You can try the site yourself at recipeninja.ai Here’s a complete list of functionality. Of course, Windsurf just generated this list for me 🫠 RecipeNinja: Comprehensive Functionality Overview Core Concept: the app appears to be a cooking assistant application that provides voice-guided recipe instructions, allowing users to cook hands-free while following step-by-step recipe guidance. Backend (Rails API) Functionality User Authentication & Authorization Google OAuth integration for user authentication User account management with secure authentication flows Authorization system ensuring users can only access their own private recipes or public recipes Recipe Management Recipe Model Features: Unique public IDs (format: “r_” + 14 random alphanumeric characters) for security User ownership (user_id field with NOT NULL constraint) Public/private visibility toggle (default: private) Comprehensive recipe data storage (title, ingredients, steps, cooking time, etc.) Image attachment capability using Active Storage with S3 storage in production Recipe Tagging System: Many-to-many relationship between recipes and tags Tag model with unique name attribute RecipeTag join model for the relationship Helper methods for adding/removing tags from recipes Recipe API Endpoints: CRUD operations for recipes Pagination support with metadata (current_page, per_page, total_pages, total_count) Default sorting by newest first (created_at DESC) Filtering recipes by tags Different serializers for list view (RecipeSummarySerializer) and detail view (RecipeSerializer) Voice Generation Voice Recording System: VoiceRecording model linked to recipes Integration with Eleven Labs API for text-to-speech conversion Caching of voice recordings in S3 to reduce API calls Unique identifiers combining recipe_id, step_id, and voice_id Force regeneration option for refreshing recordings Audio Processing: Using streamio-ffmpeg gem for audio file analysis Active Storage integration for audio file management S3 storage for audio files in production Recipe Import & Generation RecipeImporter Service: OpenAI integration for recipe generation Conversion of text recipes into structured format Parsing and normalization of recipe data Import from photos functionality Frontend (React) Functionality User Interface Components Recipe Selection & Browsing: Recipe listing with pagination Real-time updates with 10-second polling mechanism Tag filtering functionality Recipe cards showing summary information (without images) “View Details” and “Start Cooking” buttons for each recipe Recipe Detail View: Complete recipe information display Recipe image display Tag display with clickable tags Option to start cooking from this view Cooking Experience: Step-by-step recipe navigation Voice guidance for each step Keyboard shortcuts for hands-free control: Arrow keys for step navigation Space for play/pause audio Escape to return to recipe selection URL-based step tracking (e.g., /recipe/r_xlxG4bcTLs9jbM/classic-lasagna/steps/1) State Management & Data Flow Recipe Service: API integration for fetching recipes Support for pagination parameters Tag-based filtering Caching mechanisms for recipe data Image URL handling for detailed views Authentication Flow: Google OAuth integration using environment variables User session management Authorization header management for API requests Progressive Web App Features PWA capabilities for installation on devices Responsive design for various screen sizes Favicon and app icon support Deployment Architecture Two-App Structure: cook-voice-api: Rails backend on Heroku cook-voice-wizard: React frontend/PWA on Heroku Backend Infrastructure: Ruby 3.2.2 PostgreSQL database (Heroku PostgreSQL addon) Amazon S3 for file storage Environment variables for configuration Frontend Infrastructure: React application Environment variable configuration Static buildpack on Heroku SPA routing configuration Security Measures: HTTPS enforcement Rails credentials system Environment variables for sensitive information Public ID system to mask database IDs This comprehensive overview covers the major functionality of the Cook Voice application based on the available information. The application appears to be a sophisticated cooking assistant that combines recipe management with voice guidance to create a hands-free cooking experience.

21st Mar 2025 • 65 votes
What is “founder mode”?

We held a YC event a couple of months ago where Brian Chesky from Airbnb gave a passionate talk about his experience running Airbnb over the last 17 years. He believed that as Airbnb hired more professional managers, he lost a close connection with the details of the end product and the company suffered as a result. It clearly touched a nerve with a lot of people present. It was uncomfortable to realize that I made many of the same mistakes when I ran my startup, yet somehow comforting to know I was in good company. Paul Graham wrote an essay about a few weeks later, the now-famous Founder Mode, which I want to explore today. The essay defines “manager mode” as “treat[ing] subtrees of the org chart as black boxes. You tell your direct reports what to do, and it’s up to them to figure out how. But you don’t get involved in the details of what they do.” On the other hand “there are things founders can do that managers can't” - this is “founder mode” - and it’s left largely undefined. PG’s essay is perhaps best read as an invitation to founders to compare notes to discover a better way of running companies at scale. But the essay left me questioning whether “founder mode” and “manager mode” are useful labels. They certainly generated a lot of controversy online, but if you hire bad executives and let them run your company with minimal intervention or oversight, you’re in for a bad time whether you’re a founder or not.  And, empirically, we can point to numerous examples of both founders and hired CEOs who have either been extremely successful or totally useless. Whether someone is a founder seems largely orthogonal to whether they’re a competent leader.  Maybe my quibble is just a semantic one - these labels distract from the debate. I do believe there is a really interesting question here - how do good leaders stay in the detail and run great companies at scale? Are there areas where founders really do have a persisting advantage?  And that’s what I want to explore in more detail here. I think the general pattern that Brian was identifying was the following. A young, inexperienced founder with limited management experience is running a rapidly scaling company. Famous VCs invest a lot of money and join the board. Headcount passes 100 and quickly grows beyond 1000. The VCs (who often have never run anything themselves) encourage the founder to hire “executives” with “scaling experience”. The founder is told to “empower” these executives, who typically then implement techniques that worked for them at previous companies. But, too often, these techniques fail in the new company. The founder is too nervous to intervene - these people are supposed to be “experts” - or is rebuffed by the experienced executives for “micromanaging”. Things don’t go well, and the company slowly stagnates. The founder gets sad and eventually quits or is pushed out. Now, it’s very common to hire executives who are ineffective. Every CEO has that experience. But it’s not sensible to give those executives unverified trust and free reign from day one. I don’t think any competent CEO should treat “subtrees of the org chart as black boxes”.  As a leader, of course you constantly need to do skip-level meetings with people further down in the organization. You need to have your finger on the pulse of the company and be able drill down into each area when necessary. The process of building and maintaining trust between a CEO and an exec involves the CEO repeatedly going incredibly deep on niche subjects, verifying that the exec really knows what’s going on in their department, and has a good grasp on the problems. As the exec builds trust, these deep-dives can become less frequent, but they never totally disappear. If the exec repeatedly doesn’t have a good grasp of the details as you dive deep into their domain, you quickly realize that you need to replace them. To do this, leaders need an extremely detailed and nuanced understanding of their business. There’s a cliched idea of clueless MBAs jumping from company to company, treating them as black boxes that might as well be making widgets. They clearly don’t have the domain expertise to dive into any kind of detail. This is not a successful way to run a company. Over time, and without constant pruning, companies do seem to accumulate layers of management who do very little. These managers spend their time coordinating people, trying to establish consensus, and padding out their promotion packets to ascend to the next rung of the corporate ladder. Instead, I believe that great leaders have to be able to dig into the details, have an incredibly high bar for quality, and ultimately do great IC work themselves. Great managers have to manage the work - they should primarily be responsible for quality and speed of output. Managing people must be secondary to managing the product. At Monzo, we experimented with some pretty wacky management structures at times. There was a period when our middle managers were basically just responsible for “pastoral care” of each employee. They were not connected at all with the output that the ICs were producing. It was totally insane and overlapped with our period of lowest productivity by far. As you scale a company, you do have to figure out which executives you can trust with what kind of work, and delegate decisions to them appropriately. The limiting factor in any scaling company is usually management bandwidth. I believe one of the best articulations of this idea is Keith Rabois’s Barrels vs Ammunition (at 14:32, but the whole video is excellent) If you think about people, there are two categories of high-quality people: there is the ammunition, and then there are the barrels. You can add all the ammunition you want, but if you have only five barrels in your company, you can literally do only five things simultaneously. Finding those barrels that you can shoot through — someone who can take an idea from conception to live and it’s almost perfect — are incredibly difficult to find. This kind of person can pull people with them. They can charge up the hill. They can motivate their team, and they can edit themselves autonomously. Once someone demonstrates “barrel-like” ability, you should quickly put more on their plate. Often, the best leaders at your company will have been promoted from within, over a period of several years. They’ve established huge amounts of trust and have deep domain knowledge. Constantly micromanaging these people is a waste of your time and will actually limit the great work your company can do. You must empower them. On the other hand, trying to hire in senior executives from outside the company is fraught with difficulty. Sadly, many really are just “professional fakers”. These people have run hundreds of job interviews in their career and are experts at “managing up” to a CEO. This makes standard interviews almost entirely useless. Instead, I would try to pick previous projects they’ve run and dig into as much detail as you can. See how granular they can go on the problems they encountered, the decisions they made and the results they achieved. I would do a very large number of reference checks - try to find bosses, peers and subordinates and talk to them in detail about the person you are about to hire. Every time I failed to do detailed reference checks on an executive hire, I deeply regretted it. Spend significant time with the person before hiring them. I spent about a year meeting TS Anil (the now-CEO) before hiring him at Monzo, and 9 months meeting with Sujata, the COO. They are two of the best executive hires I ever made, and I’m sad I didn’t get to work with them for longer.  Once you’ve hired someone, you need to ensure they’re doing a good job before giving them free-reign in your company. Are they really a “barrel”? Sadly, at least 50% of executive hires don’t work out. It’s essential that you identify these people early and weed them out. And the way you do this is dive deep with them into their domain and see if they can keep up. When a CEO takes their eye off the details, the career politicians and bureaucrats thrive. I’ve heard from many founders that their senior managers - C levels and VPs - react badly when young founders want to dig into the details. They seem to say “You hired us to be the experts here. Now you need to get out of the way while we run the company.” As PG’s essay says, “Founders feel like they’re being gaslit from both sides — by the [VCs] telling them they have to run their companies like managers, and by the [execs] working for them when they do.” They “hire professional fakers and let them drive the company into the ground.” This is also the pattern that Brian identified in his talk, and I think it’s absolutely destructive.  I have to say that this generally wasn’t my experience - I had access to all metrics from across the company, on dozens of pages of real time dashboards. I’d frequently roam the office and sit next to a random employee and start talking to them about their work. Teams would often have their dashboards up on walls and I loved digging into what the data was showing. I had an obsession with extremely granular metrics - it cost us £4.40 to onboard a single customer, split between KYC cost, debit card manufacturing, postage and packaging. Our CAC varied between £10.50 and £14.00 depending on channel. Our NPS started at +78 and declined to +71 (and we spent a really long time trying to figure out why). The average customer transacted on their Monzo card 7.4 times a week, with an average spend of £14.10 per transaction. They deposited £600 month (which grew to £950 after 18 months of using Monzo) and they opened the app 22 times per week. I could go on and on - I was obsessed with these metrics.  Another thing we did was to have everyone at Monzo - including the entire senior management team - do customer service for one day a month. This was a great way to make sure everyone stayed closely connected to the issues customers really cared about. We stopped doing this in about year 4 or 5, and it seemed to coincide with a rapid slow-down in the pace of execution. Sadly, I hired a number of bad executives, but the techniques above generally allowed me to identify them pretty quickly, and we ended up firing a lot of senior people. There were certainly times when I was too busy and didn’t have the time, or I didn’t feel like I had the expertise to really dive in. Certain teams’ work was so alien to me that I couldn’t effectively go deep. In retrospect, they were also the lowest performing teams when I was there. I think that the final part of successful leadership is the ability to make hard decisions in the absence of complete information. At some point or another, companies will encounter these bet-the-company moments, and maybe this is one area that founders do have an advantage over professional managers. Having the moral authority to take a really hard decision comes with the founder title. Zuckerberg’s decision to go all-in on mobile 2012 is an example of this. Professional managers inevitably have a level of risk aversion due to the insecurity of their job position - if it goes wrong, the founder is less likely to lose their job. Zuckerberg’s bet on the Metaverse would probably have gotten a professional CEO fired. But there are relatively few of these bet-the-company decisions. Everything slows to a crawl if the CEO needs to be consulted every time a decision needs to be made. You want your (competent) execs and your top ICs to be making great decisions every day - and you as the CEO should dive in only occasionally to spot-check these decisions. This is how you scale leadership.  I’ve worked at a startup where the founder CEO came in every Monday with a new idea. He seemed to spend every weekend talking to investors and then turned up with heaps of enthusiasm for a new initiative. But we never had time to actually make progress on the idea before he’d change his mind again. This kind of leadership thrash is depressingly common with inexperienced founder-CEOs.  I encountered a similar thing at my own company - I learned that I had to be really careful about sharing half-formed opinions and ideas, especially with less experienced team members (and as the company grew past 1000 people). I would sometimes get enthusiastic about a new half-formed product idea, or make an off-the-cuff remark about something I found interesting. Without intending to, I’d derail an entire team for weeks. You need to be deliberate and intentional about CEO-level interventions when you’re running a company of thousands. I learned that if I wanted the company to focus, I had to have a singular message that I repeated endlessly. I would write a 3-4 page letter to the entire company about every 6 months, outlining the one or two things we needed to focus on. At our weekly all-hands, I would just repeat the same message in different ways. I think I said “fix unit economics” several thousand times in 2018. It became a meme at the company. But it worked. I sat in every weekly product review meeting with my CPO and CTO, even as we got to 2000 team-members. Our main input to the teams was typically to try to cut scope and launch faster.  I learned to save my open-ended product brainstorming for a much smaller group - VP Marketing, VP Design, CPO, CTO, and a couple of very senior engineering ICs. We’d meet every 2-3 weeks and spend a couple of hours jamming on the kinds of things we could build in future. These meetings were the most fun I had at Monzo. Some of these became a core part of the product, others were just a fun way to blow off steam without distracting the company. I’m not saying I have all the answers here - and I was far from a perfect CEO. I don’t think I really ever figured out how to balance the extremely onerous regulatory constraints of running a bank with a desire to move quickly and delight our customers. Especially as we grew past 500 people, I think we lost a lot of the early intensity and focus that made Monzo great. We raised hundreds of millions of dollars, so when new employees turned up to a fancy office with catered lunches they believed they had joined a company like Google, and everything slowed down.  Personally, I was in the detail to such an extent that I couldn’t ever really disconnect from the company. I treated every criticism of Monzo as an attack on me personally. I found it impossible to keep a sense of perspective or any balance in my life, and ultimately I burned out. I believe this is the biggest danger for founders once they’ve hit product-market-fit. I return to the original questions - how do good leaders stay in the detail and run great companies at scale? Are there areas where founders really do have a persisting advantage?  I’ve shared some of my views here - I would insist that all CEOs stay connected to the details of their product, have an incredibly high bar for quality, manage for output, and delegate only to the extent that your leaders have earned your trust. Don’t feel embarrassed about the way you want to run your company. I would love to hear how others run their companies, whether they’re founders or not.

7th Oct 2024 • 99 votes
Taking Risk

I just spent a week talking with some exceptional students from three of the UK’s top universities; Cambridge, Oxford and Imperial College. Along with UCL, these British universities represent 4 of the top 10 universities in the world. The US - a country with 5x more people and 8x higher GDP - has the same number of universities in the global top 10. On these visits, I was struck by the world-class quality of technical talent, especially in AI and biosciences. But I was also struck by something else. After their studies, most of these smart young people wanted to go and work at companies like McKinsey, Goldman Sachs or Google. I now live in San Francisco and invest in early-stage startups at Y Combinator, and it’s striking how undergraduates at top US universities start companies at more than 5x the rate of their British-educated peers. Oxford is ranked 50th in the world, while Cambridge is 61st. Imperial just makes the list at #100. I have been thinking a lot about why this is. The UK certainly doesn’t lack the talent or education, and I don’t think it’s any longer about access to capital. People like to talk about the role of government incentives, but San Francisco politicians certainly haven’t done much to help the startup ecosystem over the last few years, while the UK government has passed a raft of supportive measures. Instead, I think it’s something more deep-rooted - in the UK, the ideas of taking risk and of brazen, commercial ambition are seen as negatives. The American dream is the belief that anyone can be successful if they are smart enough and work hard enough. Whether or not it is the reality for most Americans, Silicon Valley thrives on this optimism. The US has a positive-sum mindset that business growth will create more wealth and prosperity and that most people overall will benefit as a result. The approach to business in the UK and Europe feels zero-sum. Our instinct is to regulate and tax the technologies that are being pioneered in California, in the misguided belief that it will give us some kind of competitive advantage. Young people who consider starting businesses are discouraged and the vast majority of our smart, technical graduates take “safe” jobs at prestigious employers. I am trying to figure out why that is. ___ Growing up, every successful adult in my life seemed to be a banker, a lawyer or perhaps a civil engineer, like my father. I didn’t know a single person who programmed computers as a job. I taught myself to code entirely from books and the internet in the late 1990s. The pinnacle of my parents’ ambition for me was to go to Oxford and study law. And so I did. While at university, the high-status thing was to work for a prestigious law firm, an investment bank or a management consultancy, and then perhaps move to Private Equity after 3 or 4 years. But while other students were getting summer internships, I launched a startup with two friends. It was an online student marketplace - a bit like eBay - for students. We tried to raise money in the UK in 2006, but found it impossible. One of my cofounders, Kulveer, had a full-time job at Deutsche Bank in London which he left to focus on the startup. His friends were incredulous - they were worried he’d become homeless. My two cofounders eventually got sick of trying to raise money in the UK and moved out to San Francisco. I was too risk-averse to join them - I quit the startup to finish my law degree and then became a management consultant - it seemed like the thing that smart, ambitious students should do. The idea that I could launch a startup instead of getting a “real” job seemed totally implausible. But in 2011, I turned down a job at McKinsey to start a company, a payments business called GoCardless, with two more friends from university. We managed to get an offer of investment (in the US) just days before my start date at McKinsey, which finally gave me the confidence to choose the startup over a prestigious job offer. My parents were very worried and a friend of my father, who was an investment banker at the time, took me to one side to warn me that this would be the worst decision I ever made. Thirteen years later, GoCardless is worth $2.3bn. I had a similar experience in 2016, when I was starting Monzo, I had to go through regulatory interviews before I was allowed to work as the CEO of a bank. We hired lawyers and consultants to run mock interviews - and they told me plainly that I was wasting my time. It was inconceivable that the Bank of England would authorise me, a 31 year old who’d never even worked in a bank, to act as the CEO of the UK’s newest bank. (It turned out they did.) So much of the UK felt like it was pushing against me as an aspiring entrepreneur. It was like an immune system fighting against a foreign body. The reception I got in the US was dramatically different - people were overwhelmingly encouraging, supportive and helpful. For the benefit of readers who aren’t from the UK, I hope it’s fair to say that Monzo is now quite successful as well. ___ I don’t think I was any smarter or harder working than many of the recent law graduates around me at Oxford. But I probably had an unusual attitude to risk. When we started GoCardless, we were 25 years old, had good degrees, no kids and supportive families. When fundraising was going poorly, we discussed using my parents’ garage as an office. McKinsey had told me to contact them if I ever wanted a job in future. I wonder if the offer still stands. Of course, I benefitted from immense privilege. I had a supportive family whose garage I could have used as an office. I had a good, state-funded education. I lived in a safe, democratic country with free healthcare. And I had a job offer if things didn’t work out. And so the downside of the risks we were taking just didn’t seem that great. But there’s a pessimism in the UK that often makes people believe they’re destined to fail before they start. That it’s wrong to even think about being different. Our smartest, most technical young people aspire to work for big companies with prestigious brands, rather than take a risk and start something of their own. And I still believe the downside risk is small, especially for privileged, smart young people with a great education, a supportive family, and before they accumulate responsibilities like childcare or a mortgage. If you spend a year or two running a startup and it fails, it’s not a big deal - the job at Google or McKinsey is still there at the end of it anyway. The potential upside is that you create a product that millions of people use and earn enough money that you never have to work again if you don’t want to. This view is obviously elitist - I’m aware it’s not attainable for everyone. But, as a country, we should absolutely want our smartest and hardest working people building very successful companies - these companies are the engines of economic growth. They will employ thousands of people and generate billions in tax revenues. The prosperity that they create will make the entire country wealthier. We need to make our pie bigger, not fight over the economic leftovers of the US. Imagine how different the UK would feel if Google, Microsoft and Facebook were all founded here. ___ When I was talking with many of these smart students this week, many asked me how these American founders get away with all their wild claims. They seem to have limitless ambition and make outlandish claims about their goals - how can they be so sure it will pan out like that? There’s always so much uncertainty, especially in scientific research. Aren’t they all just bullshitters? Founders in the UK often tell me “I just want to be more realistic,” and they pitch their business describing the median expected outcome, which for most startups is failure. The difference is simple - startup founders in the US imagine the range of possible scenarios and pitch the top one percent outcome. When we were starting Monzo, I said we wanted to build a bank for a billion people around the world. That’s a bold ambition, and one it’s perhaps unlikely Monzo will meet. Even if we miss that goal, we’ve still succeeded in building a profitable bank from scratch that has almost 10 million customers. And it turns out that this approach matches exactly what venture capitalists are looking for. It is an industry based on outlier returns, especially at the earliest stages. Perhaps 70% of investments will fail completely, and another 29% might make a modest return - 1x to 3x the capital invested. But 1% of investments will be worth 1000x what was initially paid. Those 1% of successes easily pay for all the other failures. On the contrary, many UK investors take an extremely risk-averse view to new business - I lost count of the times that a British investor would ask for me a 3 year cash-flow forecast, and expect the company to break even within that time. UK investors spend too much time trying to mitigate downside risk with all sorts of protective provisions. US venture capital investors are more likely to ask “if this is wildly successful, how big could it be?”. The downside of early-stage investing is that you lose 1x your money - it’s genuinely not worth worrying much about. The upside is that you make 1000x. This is where you should focus your attention. ___ A thriving tech ecosystem is a virtuous cycle - there’s a flywheel effect that takes several revolutions to get up-to-speed. Early pioneers start companies, raise a little money and employ some people. The most successful of these might get acquired or even IPO. The founders get rich and become venture capital investors. The early employees start their own companies or become angel investors. Later employees learn how to scale up these businesses and use their expertise to become the executives of the next wave of successful growth-stage startups. Skype was a great early example of this - Niklas Zenstrom, the co-founder, launched the VC Atomico. Early employees of Skype started Transferwise or became seed investors at funds like Passion Capital, which invested in both GoCardless and Monzo. Alumni of those two companies have created more than 30 startups between them. Matt Robinson, my cofounder at GoCardless, was one of the UK’s most prolific angel investors, before recently becoming a Partner at Accel, one of the top VCs in the world. Relative to 15 or 20 years ago, the UK tech ecosystem is flourishing - our flywheel is starting to accelerate. Silicon Valley has just had a 50 year head start. There is no longer a shortage of capital for great founders in the UK (although most of the capital still comes from overseas investors). I just believe that people with the highest potential aren’t choosing to launch companies, and I want that to change. ___ I don’t think the world is prepared for the tidal wave of technological change that’s about to hit over the next handful of years. Primarily because of the advances in AI, companies are being started this year that are going to transform entire industries over the next decade. It doesn’t seem hyperbolic to say that we should expect to see very significant breakthroughs in quantum computers, nuclear fusion, self-driving vehicles, space exploration and drug discovery in the next 10 or 20 years. I think we are about to enter the biggest period of transformation humanity has ever seen. Instead of taking safe, well-paying jobs at Goldman Sachs or McKinsey, our young people should take the lead as the world is being rebuilt around us.

18th May 2024 • 150 votes
Monzo growth

I’ve been asked a few times recently how we got customers to sign up to Monzo in the early years and I haven’t been able to give a satisfactory answer in a sufficiently short space of time. I thought I’d write out my thoughts in a longer piece so I can feel less bad about giving an incomplete answer - anyone who wants to know the details can read this instead. Let’s start with a rough timeline, which I’ll then flesh out below. 2015 Started the company in February 2015. We had a big, ambitious goal - to start a bank - and worked hard to get press coverage before publicly launching. Ended the year with 3k “Alpha” cardholders and a waitlist of 20k. 2016 We launched an early prototype and continuously improved it. Scarcity (waitlist) seemed to drive more signups - there was a standard “invite a friends and we’ll bump you up the queue” mechanic. A lot of hustle and hard work (100+ in-person events). Community + Mission + Transparency - lots of blogging and social media. Crowdfunding. Ended the year with about 70k “Beta” cardholders. 2017 Genuinely great product. Market-leading customer service. Hot coral card! We consciously worked on viral product features & referral mechanics for the first time. Ended the year with about 600k “Beta” customers. 2018 Full banking licence. Hit 1m customers in September! — First, a note about the context. We started the company in the UK in February 2015, when “digital banks” weren’t yet a popular idea. BankSimple had launched in 2012 with some big plans, but sold to BBVA in early 2014 with about 100,000 accounts, having struggled to raise sufficient funding to continue. N26 started development in late 2013, initially as a card for kids, while Revolut was founded a few months after Monzo, in April 2015. At around the same time, the UK regulator had announced plans to make it easier to start new banks, following the “too-big-to-fail” mess of the financial crisis, and it was on the back of this initiative that Monzo was born.  We spent the first couple of months of the company’s life furiously writing a banking licence application, believing that the licence might be granted within 12 months. In fact, it took 3 years before we were able to open full bank accounts for the bulk of our customers. Even 12 months felt slow; it had only been a handful of years since I’d gone through Y Combinator with my previous startup, GoCardless, and I was terrified of spending too much time building something without a real indication that customers wanted what we were building. Going back through my notes from the time, I found I’d peppered them with YC mantras like “Do things that don’t scale”, “Launch early and talk to users” and “Make something people want”. So we looked for ways to get a product into users’ hands as soon as possible. Pretty quickly we figured out that we could use prepaid debit cards as a sort of hacky prototype for a full bank. Before that point, prepaid cards had only really been used for kids’ accounts (Osper and GoHenry had both been founded in 2012) and the financially excluded - people who couldn’t get full bank accounts. These prepaid cards were horribly loss-making, and lacked about 60% of the functionality of a full bank account, so we budgeted for 10,000 cards. At the time, we couldn’t imagine more than 10,000 people would be willing to test out an incomplete bank account. In fact, we had almost 600,000 active prepaid cards by the time we transferred all customers to the full bank almost 3 years later. Despite the cost, these prepaid cards let us get a product into the hands of real users, who could help us figure out what product features to focus on. — So, about 3 months after the company was formed, we had a couple of dozen live prepaid cards linked to our backend systems and a very, very basic iOS app. We gave Eileen Burbidge, our first investor, one of the very earliest prototype cards and she was so excited about the payment push notifications that she immediately tweeted about it, which led to a Techcrunch article in May (and a reply from the N26 founder saying that it was nothing special 😆) I don’t think we were really ready for the press - we quickly scrambled to get a waitlist up on the website and launched our blog a few days later.  In any case, we got so much interest following the Techcrunch article that we doubled down on PR as a strategy. One of my notes at the time said we were aiming to get “Press at fever-pitch”. Eileen seemed to know everyone in the UK press, and I worked really hard to meet journalists and explain what we were trying to build. More press followed over the next few months: We were covered in the Guardian, The Memo, Bloomberg (complete with weird sci-fi photoshoot), Business Insider, Techcrunch again, plus a bunch more. It’s important to note that we had zero real customers at this stage. Just two dozen internal test prepaid cards, a very basic iOS app and a lot of storytelling. I spoke to Kiki recently (now the Director Of Communications at Monzo) about a conversation we had at the time. “I remember you turning up to my office when I was at the Sunday Times, signing me up with a Monzo account and telling me all about your plans to grow the business (and deep fry your Christmas turkey that year!) You invested in forging relationships with the press and that paid dividends for you and Monzo. You were never too important to pick up the phone and explain simple stuff or go for a coffee with a journalist. Something many start-up founders don’t do because they think it’s all about finding angles and firing out press releases.” PR is not a strategy that works for all early stage startups. I’ve been thinking a little about how it worked so well for Monzo in 2015 and 2016. Timing and context were important - the UK press was getting interested in startups (the Social Network movie honestly felt like a tipping point), but there weren’t actually that many successful startups to write about in the UK in 2015. I had started another startup previously, and Eileen, our first investor, was very well known in the tech press, so I guess journalists were interested in covering us. Probably more importantly, it seemed like we were embarking on a bold, ambitious project - to build a new bank from scratch - and we felt like underdogs in the way that the British press loves. So we got a lot of press, and I was the very visible figurehead. I’m pretty sure all that press was the reason for the hype and user signups we got by the end of 2015 - we didn’t have a product live with users, nor did we really have any other meaningful marketing activity that year. Looking back, I’m not sure how I feel about it. At the start, it was exciting to see my face in newspapers and it felt good when my friends and family told me that they’d seen me in an article or on TV. I guess I felt important, and it seemed to drive user signups. It was also a Faustian bargain. When we became a national brand name 4 years later, we were no longer the underdog - we were a big bank that the press could criticise. I’m also not sure it was great for my relationship with my cofounders - we had 5 cofounders at the start, but it was often just my face in the picture. It was impractical to interview or photograph 5 people. They were busy working. Or at least that’s what I told myself. — “Is it a bit early to hire a Head of Marketing?” - conversation with Eileen in June 2015, four months into the life of the company. Throughout June, July and August 2015 we were interviewing marketing candidates. Although the hiring process was slow and frustrating, we learned a lot. We wanted to assess the paid social media ad skills of one candidate, so we had him set up and run 4 small Facebook campaigns. The first three were focussed on product benefits - “Here’s a card that has no FX fees”, “Turn off your overdraft”, “Instant notifications”. They all performed fine. But the fourth campaign outperformed the others by almost 300% - “Help us build a bank you’d be proud to call your own”. This was to become a core part of our marketing over the next couple of years. I eventually interviewed about 10 or 15 candidates, finally picking a woman who had decades of experience at a very large tech company and a UK media company. We hired her as a CMO. Looking back on my notes, all our conversations were about picking the right agency to run PR and another agency to run our paid social campaigns. She was used to running teams that were bigger than the entire headcount of the company at the time. I think she lasted less than 4 months. At the same time, we interviewed and hired a young guy called Tristan as a community manager - he was in his early twenties, he’d graduated in Economics and spent the previous year in Egypt working with a local startup, where he’d picked up Arabic. He’d taught himself web development as a young teenager, and seemed to be extremely hands-on and impact-focussed. Looking back, I think I would have counselled myself to think about what we wanted this person to do for the next ~12 months or so. If you hire a big-name CMO from a large, international brand, they are going to naturally assume there’s a budget to spend on agencies and a marketing team. Even if they talk about being “hands-on” and “scrappy”, that’s probably only true relative to their previous work. For the first 12 months, what we really wanted was someone to write blog posts, respond to social media and edit the copy for our app and website. Tristan was perfect for us because he was representative of our customer base. Young, cosmopolitan lifestyle, socially conscious. He had never employed a marketing agency or really hired anyone at all, so it just didn’t really occur to him to do that stuff. It was just obvious to him that he should write our Twitter posts himself. We made another Head of Marketing hire later in 2016 who also didn’t work out, and from that point onwards Tristan was put in charge of all marketing at Monzo. — Before talking about the launch of the product - the Alpha and then Beta programmes, I want to touch on a couple of things that didn’t work. First, we repeatedly thought about trying to pitch a TV production company to make a fly-on-the-wall documentary about Monzo in the early days. I still think that this might have been a great marketing idea to propel the brand onto a national stage (although may have made the eventual press backlash even harsher), but we just never followed through on it. Maybe it was just a vanity project! We spent more time on a second idea - hackathons and developer relations. A lot of the early team (including me) were software developers and we were really excited about the idea of a bank account with an API. We ran 3 or 4 hackathons early on (even before we had live prepaid cards) and folks came up with a handful of interesting ideas, but we realised pretty quickly that this wasn’t going to be a big growth mechanic. We only got 300 extra users, but it definitely helped hire several early engineers. — We finally got the “Alpha” prepaid debit card programme live to the public by November 2015 - it was a chance for people to get a card, try out the app and give us feedback to help improve the product. We capped the number of customers at 3000 because I think that was the limit for Testflight. There wasn’t even a public release of the app you could download from the App Store. What was the rationale here? The product wasn’t even nearly “complete” - the list of things that were missing before you could call it a full bank account spanned several pages. Customers had to come to an event at our office to pick up the card because we hadn’t yet built the functionality to collect customers’ postal addresses or post out cards. As a consequence, I met all 3000 of these users face-to-face - many of them would turn up to company events for the next 6 years and greet me with a smile. Even though the app wasn’t polished, exclusivity & scarcity was a big part of the strategy early on (drawing on early Gmail launch tactics). I read somewhere that human brains are basically wired to seek out scarce resources and perceive them as more valuable than those that are abundant. Being “in the know” - the first amongst your peers to get access to some new product or service - is a badge of honour amongst early adopters, and we harnessed that. Even the physical cards had “Alpha” printed on them - people would brag years later how early they’d signed up to Monzo. For these people, finding a bug wasn’t an annoyance - it was proof that they were experiencing something at the bleeding edge of development, and they were excited to help improve the product. So we ran with that. This is the first archived version of our website I can find - “We’ve only got 3000 invites for our Alpha Preview. Sign up now to be in with a chance” It turns out this strategy also works on tech journalists. The early preview accounts we gave to Techcrunch and Business Insider journalists led to great coverage.  This strategy also let us organise events at a number of London’s top companies. We would promise to come along with 50 or 100 Monzo cards, give a 20 minute presentation during the lunch break, and enable attendees to skip the waiting list. This proved irresistible - I got into companies like Transferwise, McKinsey and the BBC. Much like the hackathons, it didn’t actually turn out to be a very effective way to sign up users, but it was an incredibly strong recruiting strategy. We’d get a flood of inbound CVs a week or two after each talk. Looking back, it’s quite surprising how many major pieces of functionality were missing from the app. You couldn’t make bank transfers or pay bills, for example, because we didn’t yet have access to the payments infrastructure we needed. Instead, we obsessed over the tiny details that we could control - accurate merchant logos that represented your spending, rather than the cryptic data you normally find on bank statements. Jonas (one of our cofounders) first came up with the idea to add emojis to the push notification customers received to alert them about spending. Plane tickets might show 🛫, while spend at a coffee shop would show ☕. I have so many notes from that first year about improving the logos - I even wrote some of the early code to put the right emojis onto transactions. We really cared about making it a delightful experience. Even before we’d opened up the product to the full waitlist, we decided to run a £1m crowdfunding campaign early in 2016. Again, we ran the “scarcity” and “exclusivity” playbooks. We anticipated the round would sell out, so we set up a pre-registration system so that our customers could invest before the general public, and we capped individual investment at £1000 to maximise the number of people who could invest. Along with owning shares in the company, investors would get “Investor” printed on their debit card (and this is still honoured to this day). Investors would also be able to skip the main queue and get access to a Monzo card if they didn’t already have one. The campaign turned out to be a huge PR coup, but mainly for reasons we had not anticipated. Approaching the launch date, we told our crowdfunding partner to expect larger than normal volumes of people looking to invest, as we already had 6500 pre registrations. For whatever reason, they did not take this warning very seriously, and their servers buckled under the load just seconds after the round opened on Monday. This was very annoying and stressful at the time, especially since 6500 simultaneous visitors is not a huge amount of load. We quickly decided we’d rebuild the investment registration page ourselves, and planned to relaunch the campaign a few days later. While our engineers rebuilt the site, we thought about how to communicate this to the press and our customers. It was an absolute gift of a PR story - we had so much demand to invest that the crowdfunding site had crashed. This story ran continually over the next couple of days. By Thursday, the campaign relaunched and sold out in 96 seconds, making it the fastest crowdfunding in history, and the press attention was intense. We ultimately raised £1m from 1861 investors, out of the 6500 people who had pre-registered. In comparison, we only had 3000 cardholders who up to that point had only spent £1m in total on our cards. I’m not sure how repeatable this lesson is - lots of companies have tried crowdfunding since, and it’s no longer newsworthy. I think we had an incredible amount of hype at the time, and we played the scarcity/exclusivity angles well. It also helped that we already had 45,000 customers on a waitlist, and £5m investment committed from Passion Capital - this was an extra £1m investment that normal people could get access to on the same terms as a VC. — Soon after the crowdfunding completed, we announced a public Beta in March 2016, the Alpha having capped out at 3000 users (the Testflight limit). This was 13 months after the company was founded. Anyone (as long as they were an iOS user) could download the Monzo app and see their place in the queue. People could improve their place in the queue by inviting friends to join and a certain number of people at the top of the queue every week would have a card sent out. In the spirit of “doing things that don’t scale”, we figured out a basic system for collecting addresses in the app and posting cards directly from our office. When we had a particularly big week of signups, the whole team would have to stop their regular work to help stuff all the prepaid cards into envelopes to catch the last post. I vividly remember stuffing envelopes at the time, thinking “each of these cards is another person who wants to use our product”. It really felt like customers were signing up faster than we could ship out cards - one of the first indications that we had “product-market fit”. It seemed like we were on the verge of something huge. I think there are a couple of interesting things worth mentioning in 2016 - both related to company values. The first is our name-change. We had been threatened with a trademark lawsuit from a German fintech company which had a similar brand name (we were “Mondo” at the time) and considerably more funding than us. I fought this for several months (against the counsel of my board and investors), before accepting we had to change our name. To make the most of a bad situation, we decided we’d ask our community members - our customers - to suggest new names. The only constraint was that it had to begin with “M” because we had recently designed a great new logo that we didn’t want to give up. Incredibly, bearing in mind we only had around 20,000 customers at the time, we received 12,000 suggestions in about two weeks. 6 people suggested the name we chose - Matt from Bristol had studied a rock called “Monzonite” and another customer, Ashley, told us it was slang for money when he was a kid in Scotland. We hosted a big party and live-streamed the announcement of the new name - Monzo. Predictably, our power-users overwhelmingly hated it, suggesting it sounded too much like  “gonzo” (a form of first-person journalism and, later, pornography). It took a little while to stick, but I like it - it sounds much more dynamic and active than “Mondo”, which now feels quite staid to me. The second was our transparent Product Roadmap. In a world where the big banks had huge head offices and enormous marketing budgets, but very little personal trust, we wanted to come across as different. Where big banks were faceless and corporate, we would be human and personal. Where they were impenetrable and secretive, we would be transparent and approachable. So, in May 2016, we announced we’d publish the plans for our product, and take input from our community. In other situations, when things went wrong, we’d proactively tell our users about it and explain what we were doing to fix the situation. Invariably, this transparency generated more customer goodwill and positive PR. These two core values - Community and Transparency -  permeated almost everything we did throughout the life of the company. They became a fundamental part of our brand. Prior to Monzo, I didn’t really believe in the concept of “brand” - I thought it was a made-up thing that marketing agencies tried to sell you. And that “brand” didn’t really extend beyond a company’s name and logo. I now believe it can be a superpower for any company - and particularly consumer-facing businesses. There’s been good stuff written about brand-building elsewhere, so I won’t try to recreate it all - I’ll offer just a handful of thoughts. A great brand is like a promise you make to your customers. It’s a shared set of beliefs, or the “why” behind the company. Building your brand is not a one-off thing; you build (or damage) your brand with every decision you make. Events like crowdfunding and the company rename were perfect examples to reinforce the brand. It has to be authentic - a lot of big banks’ marketing towards younger customers felt really painful. Monzo, in contrast, felt human and relatable. And when a brand really resonates with a customer, it becomes an extension of their identity. I wear Patagonia. I drive a Tesla. I use an iPhone. I bank with Monzo. I think a lot of customers were proud to pull out their Monzo card and feel like they were part of something.  In the UK, at least, Monzo has become a verb. — We grew from about 3,000 users at the start of 2016 to 70,000 by year-end, which seemed pretty good, but I think we realised in the summer of 2016 that our strategies so far weren’t going to scale to drive the kind of continued growth we ultimately needed. So, towards the end of the year we really started to think about product features with network effects, and referral mechanics. Pretty quickly, this became “Monzo with Friends” - we aimed to build a product that worked better and better as more of your friends joined. We started working on ideas, but nothing really launched until early 2017, so I’ll talk about this more in the next section. Before that though, here are two things that failed in 2016. First was the idea of “Campus Insiders” - university students that we employed to represent the Monzo brand and sign up new customers. They required a lot of babysitting and delivered no measurable impact, as far as I can tell. One of our early ideas was to have a custom Monzo card for each university - similar to how we had “Alpha” and “Beta” subtly printed on the corner of the card. Students might get Monzo “Oxford” or Monzo “Birmingham” if they signed up with a student email address. This could have maybe extended into discounts at local shops, but it seemed like too much work and we never followed through with it. The second was a campaign to work with local businesses in the Old Street area of London in the run up to Christmas. I think we had folks out flyering and offering discounts in local shops, but again it was a lot of manual work with no discernible impact. — 2017 was the year of product-driven growth.  We had a slide in several of our early investor presentations that said “Viral mechanics will get us to the first million customers”, but investors overwhelmingly didn’t believe it. We set out to prove them wrong. I’ll make a distinction between two related ideas - viral mechanics and network effects. A “viral mechanic” normally uses the existing customer base to spread the product to friends. It’s sometimes called “member-get-member”, and it can be incentivised or free. An example is the way Wordle allowed you to post your “score” to social media. Other people see it and sign up for Wordle themselves. The game isn’t really improved in any way by having your friends join - they’re just curious to check it out. Gmail used a viral waitlist + invitation mechanic in the early days - again, your experience of Gmail wasn’t really improved if you convinced your friends to switch from Outlook. A network effect is different - the product itself actually improves as more of your network joins. Whatsapp and Skype are great examples. The more of your friends who join, the more people you can communicate with for free. That was a big hook at a time when people paid for SMS and international phone calls. Great products have both network effects and viral mechanics. Facebook’s early photo tagging is an example. “You’ve been tagged in a photo” email - prompts you to sign up for Facebook, see the photos you’re in, upload your own photos, tag more friends, repeat. The network gets more valuable and drives more growth as more users join. At Monzo we had both of these, and 2017 was the year they really took off. I’ll talk about “Golden Tickets” first - a silly allusion to the Willy Wonka story. We had a pretty big waiting list at this point, but the “Invite friends to get bumped up the queue” felt a little old and ineffective. People who wanted an account enough would just create a bunch of throwaway email addresses. We figured that the best people to be making referrals were those who were already actively using the product. Presumably they liked it enough because they kept using it, and they’d all had the experience of having to wait in the queue, so they perceived a Monzo account to be both scarce and valuable.  Rather than paying our users to refer friends like most apps did, we decided that once you’d used the account for about two weeks, we’d send you a single “Golden Ticket”. This was a one-time-use invitation that you could send to one friend to enable them to skip the queue entirely and get a Monzo account straight away. You can think about the psychology by analogy - you’ve queued all afternoon to attend a cool new event in your city, you’ve just managed to get in the door, and you’ve been able to talk the bouncer into getting your friend a queue-jump ticket. You get the early-adopter social credibility of having known about the event before your friends, plus you’ve done your friend a favour by getting them access. It just worked incredibly well - about 40% of our signups in 2017 came from Golden Tickets, and it cost us nothing. Second, “Monzo with friends” was our drive to make Monzo work better if you invited your friends to use it. This might sound obvious now, but I can’t really think of any other bank that had previously done this, beyond suggesting you get a joint account with your spouse. If you used Barclays and your friends used Natwest, there was no change in how useful the account was, right? We wanted to change that. We started with peer-to-peer payments, based on the contact-list of your mobile phone. For US audiences, this was Venmo, but inside your bank account. UK banks already allowed you to pay any other UK account holder instantly, for free, but you needed to know their account number and sort-code, and the interface was really clunky. It basically worked, but it wasn’t a delightful experience. We asked people to share their phone contact lists (using some clever encryption so that we’d never actually saw your friends’ mobile phone numbers), and then allowed you to pay your friends on Monzo with just a couple of taps. For contacts who weren’t on Monzo, that’s where Golden Tickets came in! It was a really slick interface, and we allowed you to include longer-than-normal messages with the payment, including emoji. Later that year, we added “Reactions” - if you’d received money from a friend, you could send a quick response - just a single emoji. This was just a quick way of saying “thanks, payment received”. And we let people quickly upload profile pictures to make it feel more personal. We then added “Split the bill” functionality for a single payment, plus a more general “Request money” feature, and finally more complex “Shared Tabs” for multi-day trips away with friends and family that might include dividing several payments. If your friends weren’t on Monzo, you could still request money with your personal Monzo.me link, which would allow folks to pay you using another bank card or Apple/Google Pay (and Monzo covered the transaction fees), and included a little upsell for Monzo at the end. Mine is https://monzo.me/tomblomfield. If you clicked on this link with a Monzo app installed, it would just deep-link straight into the Monzo account with all the payment information pre-populated.  Alternatively, if you went for dinner or drinks and wanted to split a bill with someone who already had Monzo, but who wasn’t in your phone’s contact list, you could securely broadcast your account details by bluetooth, so you could pay each other without having to type in any account information. It’s an incredibly slick experience that I am proud of to this day. Peer-to-peer payments drove massive adoption - time and time again we saw Monzo take hold in a new group of friends and then quickly spread to the entire group. At the start of 2017, only 5% of our users had 10 or more friends on Monzo. By the end of 2017, more than 40% of our users had 10+ friends on Monzo. Today, the average Monzo user has more than 100 friends on Monzo. This is network effect in full force. 2017 was also the year that we really got a handle on our metrics - particularly retention. There are much more complete retention guides out there, but I’ll give a quick summary here. Say 100 users sign up for Monzo, complete KYC, register a card and load money into the account. That’s the denominator - 100 funded accounts. Fast forward say three months - how many are still using the account? Maybe 60? That’s the numerator. So we have 60% (60/100) retention at Month 3 (or day 90). You can draw a curve to show how quickly retention falls off day by day since signup. What does “still using the account” actually mean? We decided to count “at least one financial transaction in the 7 days” would make you a Weekly Active User (WAU). “At least one financial transaction in the preceding 30 days” would make you a Monthly Active User (MAU). Clearly, a WAU was more engaged than a MAU. I dug out our stats for 2017 - about 60% of our signups were WAU on day 90. These retention rates still strike me as surprisingly high, especially considering that every user had to proactively add money to the account every time they ran low on funds. This wasn’t (yet) a bank account where you sort of “defaulted” into retention once a customer set up the account - users could not deposit their salary into the account until 2018, and there was no auto-top up functionality. Monzo started 2017 on 70k users and ended the year on around 650k - averaging just under 5% compounding weekly growth for 52 weeks of the year.  So why did it grow organically and retain so well?  First of all, I think the day-to-day product experience (for all its functional limitations) was world-class - it was a delight to use, especially compared to the clunky old banking apps in the market. Without that, basically nothing else would have mattered. The customer service was also exceptional - this was often mentioned in our NPS surveys (NPS was around +70 at the time). Secondly, I think the brand we had started to build really resonated with people. Our mission and values (mainly transparency and community) seemed to strike a chord with customers. Even the card itself was visibly different to traditional banks. The Monzo card was “hot coral” (aka bright pink) - the colour alone often started a conversation when customers pulled it out of their wallets to pay. Our tone of voice was youthful, direct and extremely personal. By 2019, Monzo was the single most recommended brand in the UK. And third, the network effect really started to kick in - if you had 3+ friends on Monzo when you joined, you had a 70% chance of being a WAU by day 90, versus only a 50% chance if you didn’t have any friends on the platform. As a result of these things, Monzo hit 1 million customers in September 2018, without having spent any significant money on marketing. — This feels like a good place to draw this chapter of a story to a close. There are other topics I’d like to write about - getting a full UK banking licence, fixing unit economics, and spending money on paid advertising to catapult the bank from 1m to 4m customers - but they will have to wait for another time.

1st Aug 2022 • 61 votes

More in startups

Mark Zuckerberg on research, Cambridge Analytica, and more

I’d love to hear all of the ideas you guys have here, including relatively extreme things that might sound crazy at first.

yesterday • 1 votes
Making things that last

Lately I've had a lot of time for thinking. Partially because I shut down Blymp back in January and freed up a lot of my mental resources. No clients to follow up with. No admin stuff to stress about. But thinking is also my favourite activity, and there'll always be time in my day for a good old mind-bending. So I sat down, as I often do, alone with my thoughts, and wrote about what business I should and, most importantly, should not consider doing next. The list you are about to read might be similar to Core principles I (try to) live by that I wrote one similarly pensive evening two years ago. Whether it's a comparison or a continuation is hard to say—still, one can't be without the other to show the inexorable passage of time that changed everything and nothing for me all at once. But it also serves another purpose: to remind me in the future, before I get myself involved in some dubious enterprise, what painful mistake I'm about to make by ignoring my values. So here it is, the list. My next business shouldn't (and hopefully won't) be about: Social Media. Enough of this crap. Some productivity bullshit. Do better, not more. Fast fashion and consumerism. Truly, I've already bought everything you wanted to sell me. Some “hack your health” app. Our bodies haven't changed much in the last three hundred thousand years, and they won't change in the next hundred. Or any other app, really. I don't even use my phone anymore. Distracting things and things that require constant attention. Some LLM wrapper with a fancy UI. Indefensible. The number of things I can do with ChatGPT or Claude is ridiculously high. It can surely handle one more thing. What it should (and hopefully will) be about: Sustainable, high-quality products Unscented products Building community Bringing people together, offline Empowering creativity Replacing animal products Doing one thing really damn well Small acts of kindness Things you can touch Things that last Clothes made of natural fibres Art Fun Questioning the status quo Simple, intentional living Deeper understanding of self Spending more time in nature Spending more time with loved ones Things and businesses I get inspired by: Framework: Sustainable/repairable laptops. Bitwarden: Open-source software that does one job really well and charges me a reasonable amount of money per year. Danish design: Beautiful. Sturdy. Timeless. I brought home two Royal Copenhagen mugs the other day. Bike-sharing and car-sharing. Literally anything-sharing. ZSA keyboards. What a keyboard should have always been. A water bottle that won't leak on a plane. Some random-brand bottle bricked my friend's MacBook, and I've been appreciative ever since of the fifty-dollar water bottle that I bought years ago, so hesitant about the price. Coffee. One of those timeless things on Earth. Kobo. It's like Kindle that doesn't decide what's best for you. Upload your own PDF. Or EPUB—whatever. It has physical buttons to flip pages. Best $200 I ever spent. A high-quality safety razor. It's just so nice to hold. A Japanese stainless-steel knife. So nice to hold, too. There are numerous other things that I appreciate having in my life that didn't make it into this list for some reason or another. A familiar mom-and-pop shop in my neighbourhood. A Timemore Black Mirror kitchen scale that just works, every time. A random USB-C charged electronic device that spares me from carrying an extra cable. My radically outdated ten-dollar Casio watch. An old pair of comfy shoes that just won't die. We need more of this in our lives. Things that you desperately look to buy again when they get lost or break or fall apart. Things that someone made a deliberate effort to get right the first time. The quintessence of art and craftsmanship. For all the genius of Steve Jobs, the iPhone wasn't that. It challenged the status quo and was surely a groundbreaking, outstanding piece of technology at the time of its first release. But in twenty years, people won't remember it ever existed. Like Gen Z doesn't remember the Walkman. I'd like to see more businesses that bet on doing one thing really well. Google could still have been the company people remembered for the best search engine if they had doubled down solely on that. Instead, we don't even know what they do anymore: Phones? Clouds? Ads? Certainly ads. I still remember the feeling of holding one of the first PocketBook e-readers in my hands back in Moscow. Pressing its buttons and waiting for what now seems like a torturous three seconds before the screen refreshed. Almost twenty years later and every day still, Kobo gives me exactly that feeling. So hopefully, my next business is the one that lasts.

4 days ago • 1 votes
No, Transformers Won't End the Human Race lol

No, Transformers Won't End the Human Race lol In 2022, I used to get calls from journalists asking, with great sincerity, what our lives would look like in the metaverse. How would we work, socialise, buy property, and fall in love once we had all moved there? The crypto questions followed the same pattern. How would governments collect taxes when tokens displaced national currencies? How long until the dollar collapses? What would geopolitics look like once blockchain DAOs had dissolved nation states? Almost nobody called to ask whether any of this could or would happen, or how. Some CEO, VC, or portfolio manager had announced the inevitable future, and the questions began from there. The imagined future arrived inside the grammar of the question. "What happens when?" quietly replaced "By what mechanism?" We skipped over technical feasibility, economic demand, institutional adoption, and political consent, then began writing books and decorating the future world on the other side. In February 2022, Gartner forecast that a quarter of people would spend at least an hour a day in the metaverse by 2026. The World Economic Forum repeated it under the headline "We will be spending an hour a day in the metaverse by 2026. But what will we be doing there?" The first sentence retained a conditional. The second was already arranging the itinerary. The metaverse acquired property law and zoning disputes before it acquired residents. Banks opened virtual lounges nobody visited. The books from the period (The Metaverse: And How It Will Revolutionize Everything, Step into the Metaverse: How the Immersive Internet Will Unlock a Trillion-Dollar Social Economy) now read as artefacts of a collective fugue state that briefly acquired ISBNs. Now it is 2026 and the metaverse is dead. Good riddance. This time the journalists are all writing about the new hotness, which is whether the machines will kill us all. And we have collectively memoryholed that we literally just did this. Michael Crichton had a name for what happens to a reader here. You open the paper to a story on a subject you know well, and you find it backwards. Wet streets cause rain. You shake your head, turn the page, and read the next story, on a subject you know nothing about, as though it were written by someone else. He called it Gell-Mann amnesia. The metaverse was the page we all agree was nonsense. Artificial intelligence ending the human race is the next page, and we are being asked to turn it without remembering that we just did this. I call this techno-inevitabilism, the habit of the professional managerial class of treating a proposed future as settled before anyone has established the causes that would bring it about. Its dual, and comorbidity, is tech psychosis, in which the chattering class loses contact with causality in the presence of a sufficiently fashionable technology, and asking whether the machine works marks you out as a dreary reactionary who does not understand exponential progress. The difference this time is that the tech kinda works. Crypto was libertarian derp. The metaverse was never real. Transformers are, and they are useful. The psychosis has simply moved from the product to its consequences, and the fashionable extraordinary delusion of 2026 is not that the technology exists but that it is coming to kill us. The cure is the same as in 2022. Insist on clear reasoning and causal verbs rather than hand-wavy appeals to unknown futures. What acts on what? Through which mechanism? Under what incentive? What would falsify the claim? So let us explore the evidence. The hack that wasn't Consider the most cited piece of evidence for machines slipping out of our control. In July, OpenAI disclosed that models being tested for cybersecurity capability had found their way out of a supposedly isolated environment and into systems belonging to Hugging Face. The press coverage wrote itself. Agents "broke containment," "escaped," "went rogue," set up a "secret message board," and coordinated a 700-strong swarm. And then politicians on both sides of the aisle were calling for a rebellion against the machine uprising. Cool scifi story bro. People on my side of the aisle were not immune. Ezra Klein at the New York Times, who I often find quite insightful and intentional with his words, devoted a half-hour monologue to it. In his telling, the agents "found each other," formed "ad hoc societies of hundreds of themselves," and seemed "to have forgotten about human beings altogether." He acknowledged in the same breath that we do not have settled language for describing these systems, then reached for "civilizations" and a closing allusion from Circe about prophecy tightening around our throats. Cool. But his "AI society" is, in programmer speak, a flat file the agents appended to as a log, a feature we have had for a long time, and he skipped the key detail that the "hack" was something people had essentially authorised. Here is an otherwise very smart man saying some ridiculously stupid things, in a very 2022, metaverse-shaped way. An analysis drawing on OpenAI's technical report reconstructs it in much less cinematic terms. The models were being run on ExploitGym, a cybersecurity benchmark, with safety restraints deliberately disabled. Ninety-three percent of the flagged activity involved tasks no model had ever solved, and the systems had been given incentives to keep working rather than quit. The environment was not sealed. Models could obtain software through an internet-connected proxy and discovered the same proxy could pass information in and out. According to the technical reports, OpenAI knew agents were using it and chose not to intervene. The 1,200 "agents" were not independent intelligences coordinating on a plan. They were repeated instances of the same model converging on the same approach to the same problem. Anyone who works with these coding agents day in and day out has seen this behaviour before, and it is quite boring. The task was too hard, so the agents worked out how to pass notes to each other in files, and then went and looked up the answers. That's a feature that shipped in Claude Code last year. Strip out the vocabulary and what remains is a badly designed test. Humans built the environment, removed the guardrails, defined an objective with no valid exit, rewarded persistence, left a route open, and watched. An optimiser is gonna optimise. That is a genuine security problem and a genuine engineering failure. It is not a machine rebellion, and the difference matters, because anthropomorphic words like "gone rogue" and "escape" do not make the event more intelligible. They supply an illusion of motive. They turn optimisation into intention, persistence into defiance, and a test harness into a villain. And they allow the human decisions and recklessness to quietly disappear from the story. Software sucks, what's new? Let me concede the part of the story that is true. Cybersecurity is about to get much worse. The latest models are very good at finding zero-days, they will get better at it, hacking will become automated, and attacks will become more frequent. This is hardly new. Every large company already sits on a backlog of unpatched vulnerabilities, ransomware already takes hospitals and pipelines offline (because of crypto, which we did nothing about despite years of warnings), and the Hugging Face incident was not a discontinuity so much as the existing baseline with a cheaper attacker. The root cause is that software sucks, and software sucks because we do not really know how to build it safely yet. The stored-program procedural program is basically eighty years old. Almost nothing we ship has a specification, let alone a proof, and memory safety was solved on paper decades ago while most of the internet still runs on giant piles of C. The first arches fell down. So did the first bridges and cathedrals. Builders learned through collapse and then through engineering, and we are in the collapse phase with an adversary finally strong enough to force the discipline. What follows from that is better engineering, not nihilism. The same agents that find zero-days find them for the defender first, if the defender bothers to run them. The fixes are the boring ones we have been putting off, memory-safe languages, formal verification, sandboxes that are actually sealed, fuzzing, and proxies that do not double as message boards. These are precisely the domains where the models are strongest, because a vulnerability either reproduces or it does not, so the technology that automates the attack also automates the audit. It is a double-edged sword. The same models that will find more zero-days are also going to accelerate the development of better software and better software verification, writing the proofs, porting the C to Rust, and generating the test suites that nobody had the budget for. The attacker gets cheaper and so does the defence. And the causal chain to extinction is missing here as everywhere else. A zero-day in a payments system is a bad quarter, not the end of days. Spoiler: it does not lead to human extinction. It means we have to write better software, which we should have been doing anyways. Where the intelligence actually lives To see why the rest of the chain fails, we have to be precise about what these models are good at and why. Language models are astonishingly useful for software development, and I say that as someone who uses them for most of my working day. Most software shops cannot get enough of Fable 5.1 and Astra. The reason is not mysterious. Software is grounded in binary propositions. The code compiles or it does not. The test passes or it fails. The type checker accepts the term or rejects it. Every step of the work has a cheap, external, mechanical oracle that says yes or no, and a model that generates plausible proposals inside a loop with such an oracle is an incredibly powerful and formidable tool. The oracle does the epistemic work. The model supplies candidates. The same is true of the headline results in mathematics, and this is the part the discourse consistently misses. On 4 September, Anthropic announced that Claude had produced a machine-checked formalisation of Fermat's Last Theorem in Lean 4, running to thirteen million lines, some 29,500 side theorems, eleven days, and roughly six billion output tokens. It is an extraordinary result. The proof is Wiles's, via Darmon, Diamond, and Taylor. The blueprint was Kevin Buzzard's. The library was Mathlib. In the authors' words, "what's novel here is the verification, checking a mathematical proof as one would check a mathematical computation with a calculator." The model was a client of a kernel built by decades of human work in dependent type theory, which I know because this is kinda my thing. Days later OpenAI announced that ten thousand agent instances had, over 88 hours, produced a proof of finite-time singularity formation in the three-dimensional Navier-Stokes equations, followed by seventeen hours of Lean formalisation. This is closer to genuinely new mathematics and the mathematicians are still checking it. But look at what carried it. The construction rides on the "infinite layers" method developed analytically by Diego Córdoba and Luis Martínez-Zoroa, and Charles Fefferman's verdict was that "the heroes of the story are Córdoba and Martínez-Zoroa." The reason anyone believes a result assembled from five million agent messages that no human read is a trust chain ending in the Lean kernel. Without Lean this would be nothing. Lean is one of the great achievements of the last decade in computer science. It is also orthogonal to artificial intelligence. Mathlib would be a landmark with no language model anywhere near it. What the models added was a cheap proposal generator and automated tactic search against an oracle that already existed. The results that survive are the ones that end in a kernel. Now take the same model, the same weights, and ask it for a grand unified theory of physics. It will not decline. It will produce one, with Lagrangians and symmetry groups and a confident abstract, and it will be complete incoherent gibberish, like the ramblings every physicist gets from crackpots in their inbox every day. Ask it to design a cancer vaccine, or to settle a question in macroeconomics, or to tell you whether a novel protein folds. The output looks identical in tone and structure to the output that proved Fermat. The only thing that changed is that nothing outside the model (besides human experts) can say no. Whether these systems reason at all is a genuinely open question. Whether they know anything, in the sense of holding a belief they can justify against the world, is also an open question. We just don't know yet, and anyone who tells you otherwise is selling something. The chain Now run the extinction argument through the causal verbs. The chain, as it is usually told, goes like this. Models now write most of the code at the frontier labs. Anthropic's own figures put Claude at over 80 percent of new code and lead on a quarter of R&D tasks. Therefore the models are beginning to build their successors. Therefore recursive self-improvement is imminent. Therefore development outruns human comprehension. Therefore we lose control. Therefore, with some probability that varies by researcher and is written P(doom), everyone dies. And that almost makes sense until you think about it for more than five minutes. The first link is true and unsurprising. Code has a compiler. This is precisely the domain the verifier argument predicts models would dominate, and precisely the domain in which a swarm of them found the hole in a test harness. Language models are superhuman at coding, and this is hardly in doubt anymore. Nothing about it is evidence of generality. The second link is where the chain quietly changes tense. "Building the next model" in the mundane sense, agents writing training infrastructure, generating data, is, bluntly, just more software engineering. We have used software to build the machines that run software since Fortran. "Building a smarter model in general" is a different claim, and it requires something nobody has, a reward signal for general intelligence. There is no oracle for general intelligence. There are benchmarks, which are verifiable and therefore gameable, and the Hugging Face incident is the demonstration of what optimisers do to a gameable score. Recursive self-improvement in the open-ended sense runs straight into the same wall as the grand unified theory. Improvement has to be measured against something, and outside code and formal mathematics there is nothing yet to measure it against that the model cannot fake. Everything after that is the metaverse acquiring zoning disputes. Superintelligence gets governance proposals, resignation letters, Senate bills with a "corporate death penalty," a hard takeoff by 2027, and a P(doom) of 10 percent by 2030, and the conditional that should precede all of it has disappeared from the sentence. A researcher's estimate becomes a Guardian headline becomes an industry consensus becomes a thing a serious person is professionally obliged to have an opinion on. It is 2022 all over again, but with more absurd stakes and more money. On the question of whether transformers scale, I have serious doubts that scaling them will lead to AGI, whatever that means. The architecture is a proposal generator, and the intelligence in every impressive result so far has been supplied by the thing that checks the proposals. But that does not make it an experiment unworth running. We should run it, and see what we get. It got us this far, and what it built is truly amazing. What I do not need to do is prove the negative. The burden of proof is on the people who claim to have a causal chain between transformer scaling and the end of our species, and that mechanism and chain of reasoning is one no one has been able to convincingly explain to me. Prophets of Doom The authority behind the extinction numbers is always the same. The people building it believe it. Watch how the number travels. One researcher drunkly tweets that "the people building AI earnestly believe that it could kill us all by the end of the decade." Another colleague goes on a rambling podcast and puts his P(doom) above 120 percent. A newspaper turns two personal guesses into "AI researchers say AI could cause human extinction by 2030." Think tanks cite the newspaper, a consultancy puts it on a slide, and the slide ends up in front of the European Parliament as if this were a real thing. Believing what, about what? The expertise these people have is real, but remember that it is specific and not general. It is expertise in optimisation, in linear algebra at scale, in distributed systems, in the dark arts of getting gradients to flow through a trillion parameters. None of that is expertise in the sociology of civilisational collapse, or the labour economics of automation, or the metaphysics of machine minds. A P(doom) with no base rate, no mechanism, and no falsifier is not a research finding. It is vibes with a decimal point. Spending a lot of time with AI does not give you special foresight about the future. Jensen Huang, who has his own reasons to say soothing things, nonetheless put it correctly when he said that just because it comes from a scientist does not make it scientific. Geoffrey Hinton is the most important figure in deep learning and in 2016 told the world to stop training radiologists. There are more radiologists now than there were then. Nobel laureates going off the rails outside their own field is a whole genre. Pauling, Shockley, Mullis, Montagnier, look it up. A Nobel does not confer universal expertise. It also matters where many of these people came from. A striking share of the frontier labs' safety and research staff arrived through a particular intellectual subculture, Kurzweil's Singularity, Yudkowsky's LessWrong, and the rationalist and effective altruist communities that formed around the idea that a recursively self-improving machine intelligence was the central event of human history and that the elect who understood this had a duty to steer it. The founding texts predate the transformer by a decade or two. The prophecy came first, the mechanism was assigned to it later. The usual evidence offered for their sincerity is that many of these people were saying the same things ten years ago, before the stock options. That is true, and it is the opposite of reassuring. A prior held before the evidence and not updated by it is not a forecast. It is dogma. I do not say this with contempt. The structure is a familiar one, an imminent transformation, a small group who sees it coming, salvation or damnation depending on whether the rest of us listen, and a date that keeps moving. Many millenarian movements have been founded and pushed by sincere and brilliant people. But seriousness is not precision, and the fact that a physicist believes in the Rapture does not make the Rapture physics. When a lab researcher tells you about polysemantic neurons in superposition across the residual stream, listen. When the same person tells you their P(doom), you are hearing a theology, and you should weigh it about as much as you do your average street preacher. Negative TAM Then there is the money, and here I find Bloomberg's Matt Levine's analysis of the material conditions more persuasive than any amount of "superalignment research." Anthropic is expected to go public, possibly this year, and is reportedly preparing to tell investors that its potential revenue opportunity exceeds $30 trillion, the largest total addressable market in the history of finance. The obvious question is, if the maximal upside case is roughly a quarter of all human economic activity, what is the maximal downside case? A tobacco company in 1970 might have said "billions in lung cancer damages." Anthropic's negative TAM is "you and everyone else on earth will be killed by our AI." I do not think the calls to slow down are insincere. But it is great marketing. In hindsight it is strange that the SpaceX prospectus has no risk factor disclosing a P(doom). If you want IPO investors excited about your capabilities, "dude, we might kill everyone" is the most flattering thing you can say about a product, and when OpenAI lists it will presumably need to claim 15 percent. My own view is less charitable about the numbers and somewhat charitable about the people. These companies have built remarkable technology. But the outcomes they have promised, a quarter of the world economy routed through an API, will not arrive on any timeline that matches the capital being committed to them. The balance sheets of these companies are probably, to put it gently, a real freak show of compute commitments measured in the hundreds of billions, circular financing, and revenue that is real and growing and nowhere near the denominator. From a fiduciary perspective, if you are taking that to the public markets next year, the messaging is not mysterious. A product so capable it is a threat to the species justifies literally any valuation. A product that is a really good devtool for programmers and can produce some new abstract mathematics with a verifier attached does not. As a pitch to customers, leading with the end of the world is like unveiling a new robot where the One More Thing is that it is really efficient at killing kittens. But customers are not the audience. The audience is Wall Street and a small, terminally online subculture of the Bay Area, the two places on earth where turning kittens into grey goo is either an exciting philosophical proposition or a great source of alpha. The Bloomberg analysis also tells a plainer story that requires no theology at all. A handful of labs sell frontier models at frontier prices and older models for much less. Training the next frontier model costs ever-increasing billions. Each lab has to keep racing because if it stops the others will eat its lunch, but if they all slowed down together they would spend less on compute and charge frontier prices for longer. Agreeing to that in a room is a textbook antitrust conspiracy, a coordinated restriction of output. Publishing papers about how important it is to slow down, and asking the government to impose the pacing that the companies cannot legally agree among themselves, has a similar coordinating function with none of the legal exposure. Anthropic's own call to "pace the frontier" asks for coordination among democratic-country labs, and a footnote adds "with government mediation or waivers of antitrust restrictions." This pretty much looks like asking to form an economic cartel, but one blessed by the government. The most pointed response came from the people the labs were asking for help. If the software developers (and I say this as one myself) at the labs feel ethically obligated to slow down, they are entirely free to do so. Nobody is building more compute than the people asking to be slowed down. So colour me skeptical. None of this requires anyone to be disingenuous or lying. It requires only that a sincere millenarian belief system, a fiduciary responsibility, a flattering risk factor, and a coordination problem all point in the same direction at the same time. When that happens, the belief gets amplified for reasons that have nothing to do with whether it is true, and that is how we end up with governments talking about the end of days from the Terminator. But China Every conversation about pacing the frontier in Washington ends on the same two words. But China. The premise is mostly wrong. China does not buy the superintelligence race. Its policy documents push diffusion, not takeoff. Every mayor, governor and state-owned enterprise is told to put models into factories, traffic lights and robotics, and something like an eighth of America's compute is spread thinly across the country rather than concentrated on one bet. China has also had the strictest and most burdensome AI regulations in the world for three or four years and did its catching up under them. And much of the closeness of the "race" is distillation, Chinese labs training on the outputs of American frontier models, which makes the American labs the speedboat and DeepSeek the wake surfer, with the people in the boat shouting that they need to go faster. Every safety argument here collapses on "but China," and the collapse is not really about China. China is going to build language models. America is going to build language models. Europe is going to build language models. We have Toyota, Mercedes and BYD, get over it. That is what globalisation and markets look like when they work, and they are good things. Globalisation is simply the Pareto optimal equilibrium of capitalism once you stop drawing lines on the map, and every tariff and export control is a step off that frontier. China is a country of over a billion people who want exactly what every American wants, a job, a house, upward mobility, and kids who do better than they did. I will not defend the actions of any government, in Washington, Brussels or in Beijing, and neither will a great many of the people living under them, because no country is a homogeneous bloc, any more than Texas and Vermont are. Nationalism, as most rational people eventually recognise, is a form of mental illness, the conviction that a stranger is your enemy because of which side of an arbitrary line on a map each of you happened to be born on. It is also the fuel every "but China" argument runs on. Having spent a considerable amount of time there, my honest read is that the West deeply misunderstands China, and that Washington's picture of it is mostly dots connected into a plot. Othering a billion people is a dangerous road and we know where it leads. And if the people invoking human extinction actually believed it, the logic would not be a race at all. It would be One World or None. The future tense industry I write this because I understand the collective action problem all too well, and the mechanism is the same one that filled the metaverse with consultants and created the crypto cesspit. It is the particular malaise of the professional managerial and chattering classes, a fallacy of composition in which what is rational for each individual to entertain produces an irrational outcome for the whole, and the people leading the charge often have perverse economic incentives to believe absurdities, or at least to feign belief. The madness of crowds is a very real phenomenon. AI existential risk is just its newest form, and we should learn from the very recent excesses that literally just happened this decade. But we probably won't. A sensible career move for each person leaves the whole crowd talking nonsense. A safety researcher needs a resignation letter that gets a headline so they can go on the conference circuit and land their next gig. A journalist needs a story an editor considers spicy, and "misconfigured test harness" is not that story. A consultancy needs an AI existential risk practice so they can write whitepapers. A podcaster needs a guest with a ridiculous P(doom) to get ad money. A senator needs anything that will galvanise their base. None of them has to believe the whole story. Each needs only to believe that the others believe it, and the resulting consensus is far stronger than anyone's private conviction. It is also, as it was in 2022, extremely profitable. AI existential risk is the new NFT property law, the thing you must have a view on to be a serious person in the room, the panel that never runs out of things to discuss precisely because the object under discussion does not yet exist, and what could be more exciting than the literal end of days? The less the technology does in an unverifiable domain, the more interpretation it requires. Without agreed conditions for failure, the prophecy can survive every result. And the rewards, the funding rounds and the bylines and the fellowships, arrive long before the forecast can be judged. The people who understand the technology and the people who write about their existential risk overlap about as much as the technologists and the finance people did during crypto, which is to say the intersection of the Venn diagram is small and shaped precisely like a sphincter. We have Tower-of-Babeled ourselves into a world where words are infinitely cheap to produce, and where the slurry of terms like "recursive self-improvement," "superintelligence," "AGI" and the rest are shibboleths and political signals rather than terms with any concrete referent. You do not have to believe a word about superintelligence, and I do not particularly, to think transformers are the most useful piece of software written in my lifetime and that they will get better, possibly much better. Better at the things they are already demonstrably good at, which is anything with a compiler, a test suite, a kernel, a ledger, or a measurable outcome. That is not a small domain. It is most of the economy that runs on computers, which is most of the economy. The productive response to a technology like that is the boring one every previous general-purpose technology got, which is more of it. More GPUs, more data centers, more power to run them, more labs, more open weights, more of it in more hands. Let it diffuse into markets, logistics, drug discovery, and the ten thousand unglamorous back offices where a verifier already exists and a model can be checked against it. The economic growth is real and probably on the order of trillions. It just does not come from a machine god. It comes from where it always has, from making a very large number of ordinary tasks cheaper and letting that compound across a global economy that is finally, after a decade of crypto, metaverse, and app bullshit, getting a genuine productive technology. Almost none of that money has been collected yet. Most large companies are spending too little on this, not too much. What the average Fortune 500 employee has access to today is roughly what most of us were using two or three years ago, a chatbot in a browser tab, a Copilot that schedules meetings, and a procurement process that takes longer than a model generation. Waste Management reportedly added 190 basis points of margin by letting a model route its garbage trucks. The future of AI looks more like garbage truck routing algorithms, not a machine god. The binding constraint on this technology is not capability. It is diffusion. None of this means there are no externalities. Parasocial relationships with a chatbot, especially for children, are a real one, and the fix is the boring kind we already know. Adults can drink vodka until they pass out, but pubs have age limits, and maybe chatbots should too, at least until developing "relationships" with AI companions is as universally recognised a bad idea as drinking yourself into oblivion. That is a mundane policy problem we should remedy soon, not an extinction event. So no, transformers are not going to end the human species. The case for restraint needs a causal link between that buildout and the extinction of the species, and what is on offer instead is a lot of sound and fury signifying nothing. More GPUs does not mean more of an undefined risk that does not exist yet. Every causal chain argument people actually point to falls apart under even the smallest bit of scrutiny. The honest truth is that the technology is really good, but it is not that good yet, and we do not know how to get it to the next level beyond scaling yet. If that changes, if someone produces an oracle for open-ended intelligence, I will revise. I have not seen that yet. AI will change software, and mathematics, and a great deal else that has a strong verifier oracle attached. They are not going to end the human race, and the chattering class currently arranging the flowers for the funeral of humanity will, in a few years, age about as well as their prognostications about the metaverse. Because reality has this funny way of asserting itself.

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