Connect the dots backwards

Steve Jobs said it in his commencement address at Stanford on 12 June 2005: "you can't connect the dots looking forward; you can only connect them looking backwards." His own example was a calligraphy class he took after dropping out of college. Nobody would have put it on a programmer's syllabus. Ten years later it came back as the typography of the first Macintosh.

If you are coming to this from somewhere else, from running a team, from a job that never involved code, or from twenty years in a field whose software you are about to build yourself, that sentence is for you. You cannot see where this goes from here. Nobody can. So you have to trust that the dots will connect, and trust your gut.

Connect the dots backwards

Trusting your gut is not the same as having no method. The newsroom already has one for walking a path you cannot see ahead of you, and the book will use it. The path is not a straight line. What matters is knowing which decisions are one-way doors and which are two-way doors, so that you go through the two-way ones quickly and think hard only about the others. It matters that you can iterate very fast. It matters that you play the game of constraints, and that you work where the smallest move shifts the most.

I have a small dot of my own from this year. I took SG/Send, a file-sending service where the server holds nothing, to market end to end: a checkout live on 1 March, ten paying customers the next day, and by 5 March a brief saying "Users buy credits, test once, and don't come back." That is where I learnt Stripe, pricing and everything else a real product needs, so by the newsroom I already knew how; and the same technology is in use now because the newsroom, RiskMandate and the vaults need it. In From Send to sgit I called it "a good example of how exploring something early pays off later, in a way nobody plans." Explorers find things early; the use case decides when they matter.

Your future self will connect the dots. Your job now is to make dots worth connecting.

The edge is multidisciplinary

What I most want to stress is that, more and more, it is the multidisciplinary person who has the edge. Not someone going in a huge number of directions at once, and not chaos. The ability to learn, and to intersect disciplines.

If you have spent years doing something other than programming, that is not a gap in your CV. It is the discipline you are about to intersect. The 2018 chapter on learning said that "being a better musician, artist, athlete, car mechanic or philosopher will make you a better developer", and that is truer now, because the wall of syntax and setup has come down, and what is left is knowing what to build and for whom. That is what you bring, and for a founder it is the advantage.

That is why the humanities matter. And it is why, in a way, mathematics matters.

We teach maths as if to make people hate it

The reality is that we do a horrible job of teaching maths. One of the books I love is A Mathematician's Lament by Paul Lockhart, an essay from 2002 and a book in 2009, which says, in effect, that if we had set out to design a system to make people hate mathematics, we could not have done better. It opens with a nightmare in which music is taught the way maths is: notation and rules for years, and never a song.

The same is true of music, and of much else. We give the solution away. We do not teach the wonder, we do not teach the struggle, and we do not let people be creative. There is a story, often retold, that children start out very creative and school beats it out of them; the retellings are better known than the original data, so I will not lean on it until I know what was actually measured.

If you hated maths at school, you were probably taught it badly. You were not born without it. The 2018 outline for a chapter called Love maths had one idea I still like: get people to do the maths that sends a rocket to space, with real problems, the way the human computers did.

Learning, made for you

Our education system was designed for a very specific type of person, with a very specific set of skills. If you were very good at maths of the school kind, at memorising, at a certain kind of academic writing, you did well. That is a small subset of the population. I was not the best at that, and I think that is why I gravitated to computing: it let me be creative, and it let me find the zone.

You know the line about judging a fish by how fast it can climb a tree. It is usually attributed to Einstein, without evidence. Judged that way, the fish believes it is stupid. The academic model was generic, made for a mythical student, too high for some and too low for others.

That has changed. This is about you. How you like to learn, how you like to take in information, how you like to get lost in something. I learn more now than I ever have, because I can shape what I learn to what I am curious about and to how I like to learn, and go deep exactly where my gaps are. And you can now build the tools for that.

There is science behind this. Mihály Csíkszentmihályi's Flow, from 1990, describes the state the 2018 book called the zone: clear goals, immediate feedback, and a challenge matched to your skill. He named programming as one of the activities that can produce it. Anders Ericsson's Peak, from 2016, is the science behind learning as a skill: expertise comes from structured practice with feedback, at the edge of what you can do, not from the number of hours. Ericsson did the research behind the ten-thousand-hours number that someone else made famous, and the credit belongs with him. The 2018 chapter on learning said the same in Kathy Sierra's words from Badass: practise right, and consume large amounts of good examples. What agents change is that both halves, the right practice and the good examples, can now be made for you.

Think with it; do not delegate your thinking to it

A search engine that gives you information from anywhere in the world was already a ridiculous technology, when you think about it, and my generation was the first to have it.

But search gave you an answer, without the ability to question it, to go deeper, to explore. Generative AI gives you the ability to really go deep: to challenge, to ask the questions, and to ask for the evidence. That is the difference between using it to skip understanding and using it to understand. If you ask it for the answer and paste it in, you have learned nothing and put your name on something you cannot defend. If you ask it why, then where that came from, then what would show it was wrong, you are learning.

It is still your responsibility. It is still your name; the work is still done under you, and if the company is yours, so is everything the agents build in it. This is not you delegating your thinking to generative AI; it is you thinking with it. I do it when I write: I ask the agent helping me with this book to double-check my quotes, to make sure what I am saying is coherent, and to find the evidence behind every statement. The name of the author of A Mathematician's Lament, which I remembered wrongly in the memo behind this chapter, was corrected that way.

And sometimes, as I always say, you use generative AI so that you do not have to use generative AI: you use it to write the script or the tool that then does the job reliably, with no model in the loop.

Generative AI lets you explore again

Exploring is back, and so is communicating in ways you could not afford before.

Look at what happened while this book was being written. I wanted a better way to communicate the book, and to think about it, than infographics, which take too long and cost a new image for every change. So I asked for something programmatic instead. What came back was a cast of stick figures drawn by code, in the browser, every time the page loads, from a data file per storyboard. The renderer is about 400 lines. The storyboards in this book are those. That is how I like to think, and that is the beauty of it: you can now discover how you like to work, and build the thing that lets you.

When people tell me they do not see it, or that they are overwhelmed, my argument is: experiment, learn, try, start, and adjust. Let your questions guide you.

Learning is a lifelong skill

Learning is a skill. Take it seriously, because it is a lifelong one. The 2018 book opened its chapter on it with a question I would still ask you first: "Do you know how to learn?" And it said what I still think: learning is like a muscle, and the more you do it, the better you become.

Historically, the roles and opportunities open to people were limited by geography, money and power. Now you can be in the middle of nowhere, with an internet connection and, less and less, a bit of compute, and have the learning systems of the world in front of you. The future, as I see it, is decentralised and sovereign: local compute, local systems. That is my position, not a forecast.

And here is a point I want you to hear plainly: by reading this book, you are already in a very small minority. The numbers behind that are the newsroom's own arithmetic on other people's surveys, for sizing, not for adding up: professional developers are about 0.6% of the world's internet users, and in the OECD's tests of adult skills across 33 rich countries, 26% of adults could not use a computer at all, and only 5% reached the level of a skilled user. You are already ahead, and the gap between you and the people who have not started is the opportunity, and the responsibility, that Civic responsibility is about.

The thinking is never wasted

For a founder, connecting the dots backwards has a practical form: a pivot leaves assets, if you did the work in the open. Two of mine, summarised from memory; the proofs of concept are not all published.

The Cyber Boardroom was a GenAI platform to help boards with cyber security, with an advisor called Athena. It taught me what personas, and output shaped to the person reading it, are worth; there is a direct line from it to the personas in RiskMandate and the newsroom today. What it needed and did not have was somewhere to keep the data: nobody wants confidential board material in an environment somebody else runs. Ironically, what it needed is what I eventually built as sgit. It was open source, so I could pivot, into MyFeeds: semantic graphs of the news, personalised all the way to the reader. That is when I realised that language models were how graphs could scale. I built a good proof of concept, took it to the industry, and there was silence: it needed micropayments, paying for the graph and paying the people who write, and the industry was not there. So I paused it.

What stayed was the thinking. The pieces I wrote in 2025, on monetising trust, on fact provenance and on micro and nano payments, are the foundations the newsroom now stands on, and I can now go back to the publishers with an end-to-end example, all running, all open source. If it does not land in 2026, I will try again in 2027. As I put it in Not a straight line: "Because it was open and written down, it was there when the moment came."

That is the founder's version of the calligraphy class. You cannot know which experiment will pay. You can make sure that when one pauses, what it taught you is written down where you, and other people, can find it.

Know where your ideas come from

One last thing, and it is an exercise.

Nothing in this book came from nowhere. The frame is Wardley's; the zone is Csíkszentmihályi's. I keep a register of my influences, as claims you can check rather than a page of warm sentences; its own first article says that "An entry in this register is a falsifiable claim about my codebase." Each entry names the one work that did the shaping, the principle it gave me in a sentence, where that principle shows up in my work, and where it is still absent. The "where this comes from" boxes in this book, and the appendix at the end, are made from it.

Do the same. Pick the three people or works that have shaped how you think. For each, write the one work that did it, the principle you took from it in one sentence anyone could use, where that principle shows up in something you have made, and where it does not yet. That is a register. It is also a CV that shows how you think, and a reading list for the next person.

And write it on paper first. The 2018 chapter Pen and Paper said it in one line: "The reality is that you will forget the ideas you are having today!" The only way to make sure your future self has them is to capture them now. Then, when you look back, you will have the dots.