Vibe coding lol

Love it or hate it, it’s here. So let’s talk about it.

Y’all may remember a previous article of mine back in 2023 called The Rise of Artisan SaaS. It was an ode to the era of high-craft software that boasted insane attention to detail, opinionated flows and micro-interactions (ie. the Linear trend).

But 2023 was a lifetime ago in “AI years”. You can think of this article as a Part II amidst the ever-changing tides of technology. Not to sound melodramatic, but today in 2025, we’re experience the beginning of a new technological shift, not unlike the invention of the printing press or blogging.

Consumption of ideas: In the 1400s, the Gutenberg press freed people from the monopoly of priests who interpreted scripture for them. Suddenly, there was an explosion of literacy as people had direct access to read, learn and, ultimately, think for themselves.

Distribution of ideas: Fast forward to the 2000s, and the internet did the same for the distribution of ideas. You no longer needed a journalism degree or a newspaper column to share your thoughts with the world. If you had a blog, you had a voice. If you had Twitter, you had an audience.

Realization of ideas: And now, we're entering a new chapter. Thanks to AI coding agents, anyone can take an idea in their head and build software to bring it to life.

Short interlude: No, I don’t believe engineering is dead

I even tried my hand at it back when GPT-4 launched.

But this… this is different. Back then you had to copy and paste all relevant files into ChatGPT to provide context to the LLM, then copy the output back into your code editor and cross your fingers that you wouldn’t run into bugs. And if you did, you had to copy the bug and paste it into ChatGPT etc. etc. etc.

Now, you just type into a chat box and let the LLM do all the work for you with the occasional, “didn’t work, try again”. So of course, I had to take another stab at it. Keep reading to the end to read about my experience “vibe coding” Paddles.ai and what I learned about keeping the AI on track.

But let’s go back to this whole technological shift thing.

The Great Debate

Historically, software development was a specialized skill set. If you had an idea, you needed to work with someone who could translate it into code. It was a slow, often expensive process that created a natural gate between those who could dream and those who could build. That’s why so many of the Y-Combinator companies started with two co-founders: a product, design or sales guy and then a technical guy. But always a technical guy.

Now, that dynamic is changing. Tools like Replit, V0, Bolt, Lovable, Windsurf, and Cursor are making it possible to speak in natural language and get working code in return. You describe what you want, and the AI does its best to make it happen.

It’s a powerful shift, especially for folks like me - designers who have just enough technical chops to be dangerous. Now, we can move from an idea to a prototype in the span of a day, without waiting on engineering bandwidth or learning a full-stack engineering. The keyword in that sentence being “prototype”.

This new unlock introduces new complexity. It lowers the floor to write working code, but doesn’t necessarily raise the ceiling. As a result, many of the apps that are yeeted out over a weekend are fragile and vulnerable.

And boy are the people taking sides!

“Vibe coding will never be more than a party trick”

“Engineering is dead - long live vibe coding”

“This LLM code is dog water!”

“I just vibe coded this app last week and made $69K”

Fast SaaS vs. Artisan SaaS

And it’s these sorts of “hot takes” that make me roll my eyes because the truth, per usual, is somewhere in the middle.

I think we can get a good grid of where the future is headed by framing it up against fast fashion. Trends come into vogue and are gone the next season. As a result, the clothing manufacturing pipeline adapted, optimizing for cost and speed knowing their garments would just end up in a landfill in 9 months.

But fast fashion didn’t kill quality brands - it just filled a different need. I have a Cahartt coat that my dad passed down to me. Believe me, he’s put that thing through the wringer over the years and it’s still a great coat! The world still needs high-craft clothing.

In the same way that fast fashion emerged from a drop in the cost and complexity of creation, so has Fast SaaS. The upside is undeniable: more people building, more ideas tested, more needs met. But just like fast fashion fills landfills with barely-worn garments, Fast SaaS will leave behind its own trail of abandoned prototypes, broken tools, and single-use apps forgotten on a server somewhere.

And that’s where high-craft software stands apart. It’s not about being slow or precious - you can still use AI to assist you and move quickly. It’s about being intentional and building for resilience. Like the old Carhartt coat my dad handed down to me.

Fast SaaS will continue to rise, and I genuinely believe that’s a good thing. But so will our appetite for tools we can rely on. The divide will just grow more stark.

The future isn’t either-or. It’s both-and.

That’s the end of my philosophizing about vibe coding but if you’re still here and want to read about my experience building an app with AI that’s more than just a prototype, keep reading. It can be done!

I’ve included the tips and tricks that I’ve learned the hard way to minimize problems and maximize quality output - hope it helps.

My Vibe Coding Experiment

Before I ever thought about building an app, I was logging my casual pickleball matches in Apple Notes. I wasn’t playing competitively, but I still wanted to know some stats just for fun like my win/loss ratio, win streaks, who I played best with in doubles etc.

Every so often, I’d copy my notes into ChatGPT and ask it to generate some stats for me. It was janky knowing what I know now, but it worked!

The more I thought about it, the more I realized there’s a slice of the Pickleball community like me that might enjoy these sorts of stats too. They’re not serious pros, but they’re not casual players either. I’ve been calling them the “casually serious” for lack of a better term. So I decided to put these AI tools to the test and scratch my own itch.

Today, there are a handful of tools out there like Lovable, Bolt, and V0, but I started working on this toward the end of 2024 and they hadn’t hit their hype-cycle just yet. Despite that, even today, I’d choose Replit for two key reasons:

1. Code transparency: Replit let me inspect and modify the code directly. If the agent generated something weird, I could go make edits myself.

2. Unified stack: Replit bundled everything I needed - database, secrets management, hosting, and deployment - into one product. For simplicity, I didn’t want to have to piece together multiple tools like Supabase and Vercel just to get something deployed.

Here’s a good comparison chart from Lenny’s Newsletter that shows what each tool supports.

Start with your stack

What followed was a lot of trial and error. I restarted the project four or five times before finding a rhythm. Replit didn’t respond well to massive prompts outlining all the functionality I wanted. So instead, I broke the process into smaller steps. I asked it to scaffold a basic app with TypeScript and Tailwind since I'm familiar with these frameworks and could step in as needed.

Update: On March 28th, Replit released Agent V2 that came with some seriously good buffs so my method of starting small with the original prompt may not be as necessary any more.

Dial in your database

I quickly realized that jumping into features too early created chaos. The database became tangled and incoherent, mostly because it was forced to make database changes on the fly for each feature (this is where a bit of technical knowledge came in handy, but hopefully you can learn from my mistakes). So I paused and built a list of all the features I wanted. Then I asked the AI to reason through the best architecture for those features. Once I had a schema I liked, I started a fresh thread, gave it the schema, and asked it to implement it cleanly.

This had a huge impact because it focused the task to just setting up the database which allows any features built later to not have to worry about that step.

Focus on your features

From there, I iterated on features one at a time, using a two-step prompting process:

1. First, give it a full description of the feature you want to build and ask it to come up with the best plan to implement it given the architecture. It’s important to also remind it to not write any code at this step. If you don’t explicitly say that, it will sometimes come up with a plan and then immediately start implementing it.

2. Next, start a brand new thread to actually build the feature. The reason is that when it was reasoning through the plan in the previous step, it added a ton of files to its context window which means you’ll hit the limit of what you can accomplish in that thread super quickly. Now, with a clean context window, you can just paste in the plan that it gave you and it usually can one-shot it pretty well. If the plan is more complex (5+ steps), I broke it into smaller chunks and pasted each chunk into a new thread.

Clean up your code

Every few features, I asked the AI to run a cleanup pass - consolidate logic, remove unused code, clean up any logging used for testing, and rewrite comments for clarity. With each new feature, the AI would occasionally leave behind outdated code or comments which confused future prompts so this just cleaned it up so it wouldn't get confused in future passes.

Babysit your bugs

Now let’s talk about bugs. Most bugs don’t happen in the feature that being implemented. The LLM actually does a pretty solid job of writing the code from the plan you gave it. Most bugs come from these weird little tangents the Agent gets on where it deletes or edits code that has nothing to do with the task at hand lol so annoying.

The best way to avoid these types of bugs is to catch them as they’re being written (aka, babysitting the AI). I keep an eye on which files it’s editing in real-time, and if it starts making changes to files that aren’t relevant to the current feature, I immediately pause it, ask it to revert the changes, and remind it to stay focused on the task at hand. That usually works. Worst case scenario, I look at the change log and revert the changes manually myself if it’s having trouble.

Since December, I've been shipping a feature or two each week and, because Replit handles hosting and deployment for me, I can push updates immediately without worrying about devops stuff. I know I just talked a lot about how to minimize AI screwups, but it’s honestly wild that it’s this easy. I’ve had so much fun with it and at the end of the day, shipped something that’s super useful to me and others.

What Comes Next

We're entering an era where anyone can build software.

But not everyone will build great software.

I’m anticipating a flood of weekend projects that are half-baked and never maintained. And that’s not necessarily a bad thing. Sometimes, utility has a short half-life.

But amidst that flood, there will be a renewed appreciation for software that lasts. For the digital Carhartt coats. For apps that reflect the care of their creators.

Artisan SaaS isn’t just about aesthetic design anymore. It’s about architecture. Durability. Security. Performance. It’s about software that’s not just written, but crafted.

That’s the future we’re building.

—Jacob ✌️

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When I first heard about 3D printing, my mind immediately jumped to a future where we could print anything we wanted in our home without having to drive to the store. Of course, that dream was short-lived as I realized I'd be 3D printing a bunch of parts over the course of days and then assembling the item myself. Not to mention, I'd still end up with a bunch of random 3D printed things that I only used once, shoved in a closet somewhere in my house.

3D printing might not be the answer, but perhaps the future I dreamed of isn't as far away as I thought. A future where we could get any item we needed near-instantaneously. Maybe I couldn't print the things I needed, but maybe I could get it delivered...

Let's dive in!

The Problem

In case it wasn't obvious, delivery logistics are incredibly difficult. If your delivery network isn't built out, the costs are astronomically high and, depending on the price, sometimes more than the item you're shipping.

🚙 Crowd sourcing?

Companies like Instacart first tried solving the last-mile delivery problem by crowdsourcing it. If every person with a car could be a delivery driver, you could scale your fleet without putting more cars on the road.

But even if your delivery network is well-developed, the last-mile costs tend to make up around 40% of the total shipping costs due to the inefficiencies of traversing a city. Rather than going 70mph on the interstate in a straight line, you're zigging and zagging through city streets at 35mph, stopping at traffic lights and getting stuck behind traffic.

🚁 Drones?

Companies like Amazon are trying to solve this problem with drones that can make deliveries "as the crow flies." If it can skip the traffic, speed limits and stop signs, deliveries can be made from warehouses on the outskirts of the city directly to its destination. But even drones are limited by speed, winds, precipitation and the FAA.

In any case, until delivery drones become ubiquitous, delivery services end up having to impose cart minimums or batch multiple orders with a shared destination in order to make a same-day delivery worth it. And for the companies like Amazon or Walmart who do offer free or affordable same-day delivery with no cart minimums, you can guarantee they're losing money on smaller purchases in order to maintain customer loyalty and satisfaction.

Despite the lingering problems, not all hope is lost. Maybe it's not a pipe dream after all.

Do y'all remember transport tubes from the 1960's TV show, The Jetsons?

Or the mail tubes from the post office?

Or perhaps the drive-thru tubes at your local bank?

What if we had that... but bigger?

There's a company called Pipedream that's working to build an underground network of tunnels for delivering physical goods. They’re not pneumatic tubes like at the bank drive-thru, but the idea is similar. These tunnels would connect public spaces, offices, retailers and, eventually, even homes allowing goods to be delivered lightning fast throughout the city.

The tubes they’re using are the same sort of PVC tubes that utility workers are already installing all around the modern city. Thanks to advancements in robotics and small motors, they've engineered a tube-dwelling courier robot that can travel at speeds up to 110mph on the straightaways, bringing physical goods from one portal to another with zero traffic, zero speed limits (other than those imposed by physics), zero stop lights, and zero weather concerns.

Consumerism? Or minimalism?

I can hear the haters already. Doesn't near-instant delivery just enable higher levels of consumerism and “I want it now” mentality?

The unfortunate truth is that, in the beginning it probably will. Cheap, instant deliveries? Immediate gratification? I think we can expect that a consumerist response will most likely be the case.

But in the long-run - and this is me just catching the vision for Pipedream - I believe cheap, fast, in-home deliveries will encourage a minimalist, possession-light way of life.

Hear me out.

Let's say you're planning to re-tile your shower and you need a grout float, a notched trowel and a pair of tile nippers.

Sure, you could run to Lowes or Home Depot and pick up a set. But now you've got these three tools that you'll probably never use after this project. They're probably not worth selling on Facebook marketplace and you don't want to throw away perfectly good tools. So they end up in your garage or a utility closet somewhere, taking up space and not being put to use.

Enter: Pipedream

With a nearly free, instant, invisible delivery network, there could be a number of options for you to get the tools need without the added burden of keeping them after you’re done.

1. Perhaps you could return those items to the retailer you bought it from.

2. Maybe you simply rent them from Lowes who has their own Pipedream Portal in their Tool Rental department.

3. Or maybe there's some sort of crowdsourcing option where you could then send the tools to someone across town who's working on their own tiling project.

In any case, Pipedream would enable you to get what you need when you needed it and then return it once you're finished.

The “Physical Cloud”

I like to explain this delivery network in terms of a digital network since a lot of you are technologists. A network becomes more valuable with every node added to it. Pipedream's tube network is exactly the same way. The more nodes it connects, the more options are available for the sending and receiving of physical goods.

At some point in the networks growth, it can begin to function similar to a blockchain network. In the same way that a blockchain distributes compute and ledger activity across each node in the network, a physical network like Pipedream can distribute the storage of goods across each node in the network (eg. other people's homes, retailers, warehouses).

This turns the community you live in into a "physical cloud" where you no longer have to store goods locally in your home. Rather, you can off-load your possessions to the "physical cloud" and then retrieve those items as needed.

Sure, there will always be things that you want to keep in your home - you won't off-load your laptop or your favorite backpack. But for the non-personal items like a screwdriver, a turkey baster, or a pool float, you can simply request them exactly when you need them and receive them in minutes. One less item to clutter up your home. The beauty is that when you have a need for that random item you'll probably only ever use once or twice, you don't have to drive to the store and buy it. You can just rent it for the day.

The Impact of Near-Instant Delivery

It's going to take some time before Pipedream's dream becomes less of a pipe dream and more of a reality. But as the network grows within a city, there are a number of second-order effects that we may start to see.

1. Fewer cars

When Uber, Instacart, DoorDash and other delivery services began popping up in the 2010's, the number of cars on the street at any given time exploded. In San Fransisco (which is an edge case, of course), the SF Traffic Authority estimated that vehicle density increased by ~50% after these crowd-sourced delivery companies were started.

Until we have tubes like the Jetsons, we'll assume that Uber will still be a thing for a while. But smaller items like food and groceries could be sent through Pipedream's network which would decrease the amount of delivery drivers on the street.

2. More affordable goods

Somewhere around 40% of the cost of shipping is tied up in last-mile delivery. If you could cut that cost down to pennies on the dollar, we could see a significant reduction in shipping costs - Pipedream wants to see shipping costs drop down to $1.

3. Less waste

In an ideal world, as the network reaches maturity, fewer items would be able to satiate the same or higher demand. Here's an example from Drew, Pipedream's co-founder.

"There are maybe 10 billion screwdrivers in the world sitting in drawers and toolboxes, a fraction of a percent being used. With hyperlogistics, 2-3 million screwdrivers being constantly distributed around can satiate demand for screwdrivers. This results in billions of screwdrivers that no longer needed to be produced, billions of pounds saved from landfills, & higher quality screwdrivers."

—Drew, Co-founder and COO of Pipedream

Now, I'm not so optimistic as to believe that the 10 billion screwdrivers in the world will magically be reduced down to 3 million and the rest be recycled and kept out of landfills. However, the principle holds true. For future goods being manufactured, the number of units needed to satisfy demand will go down, the number of unused items sitting in closets will go down and, hopefully as a result, the number of items that end up in landfills will go down.

That's the future that Pipedream is building.

I’ll catch ya next week.

—Jacob ✌️

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For as long as the StarWars nerds like me have dreamed of a future with our own personal C-3POs, it's never been much more than a pipe dream. Even if we could build a replica of C-3PO and make it walk or say something in his iconic worrisome voice, we were always limited by the ability of our C3POs to see the world and interact with it. That is... until now.

AI has mostly lived behind our computer screen, only surfaced through some sort of chatbot. But it hasn't been able to engage with much else beyond the data we give it. It hasn't been able to "see" the world and interact with it.

The advancements in AI over the last year have acted as a forcing function to expedite the manufacturing of training chips, the development of powerful models, the investment of billions of dollars, and the conversations around safety, ethics and opportunity. The AI flywheel has begun to spin faster and faster, pushing the boundaries of what we thought was possible with new breakthroughs every week.

As a result, we’ve begun to see a wave of robotics companies like Tesla, Figure, Boston Dynamics, X1, Agility, Clone and Sanctuary, building robots powered by artificial intelligence. In a similar way that space exploration was relegated to government programs until companies like SpaceX began to make headway, robotics was largely the work of researchers in academia until now.

Though many of the companies I mentioned are focused on special purpose robots like Boston Dynamics' Spot and Stretch, some, like Tesla and Figure, are building general purpose robots with the ultimate vision of every person having their own humanoid robot in the same way we might have a car or a phone today.

And while that vision is fun to think about if you’ve ever wanted to tinker around with your own C-3PO like Anakin Skywalker, the impact goes far beyond simply a household bot to do your chores. In this article, I want to attempt to answer why 1) humanoid robots in particular are so important and 2) the broader impact on our world as we know it.

What used to be a dream for the nerds like me, may soon be reality for our children and grandchildren. Let's dive in.

The Challenge of a General Purpose Robot

Over the years companies like Boston Dynamics have managed to build interesting robots, including a humanoid called Atlas.

Atlas is a research project that's meant to push our understanding of what bi-pedal, humanoid robots are capable of and, to be honest, it's quite impressive. It has the ability to stabilize itself when knocked off balance, maneuver through a complex obstacle course, and even do a backflip off of a box with perfect form. So if a humanoid with this level of ability already exists, you might be asking yourself, "where's my Atlas?"

"You've all seen very impressive humanoid robot demonstrations. And that's great, but what are they missing? They're missing a brain. They don't have the intelligence to navigate the world by themselves. They're also very expensive and made in low volume."

—Elon Musk, CEO of Tesla

There are a number of reasons why robots like Atlas haven't become commercialized:

1. Cost — Creating a robot like Atlas with all of its materials, sensors, cameras, actuators, processors and batteries gets really expensive. So much so that it becomes cost prohibitive for personal and even most commercial applications.

2. Practicality — Most tasks in industry and logistics can be performed more efficiently and cheaper by simpler, specialized machines. Putting a general purpose robot on these sorts of tasks would be a waste given the immense cost.

3. Intelligence — General purpose robots aren't simply programmed to do one repetitive action over and over again. They're meant to be able to do a wide variety of tasks and adapt to its circumstances. This requires incredible decision-making abilities which simply haven't been developed yet.

To achieve the dream of giving every human their own C-3PO, we'd need to dramatically reduce the cost of production and make step-change advancements in artificial intelligence so that it could adapt itself to the world and make decisions.

The Case for a General Purpose Robot

Given the immense costs and complexity of building a humanoid robot like C-3PO, it stands to reason that we might ask "why?"

According to Figure, a company that’s working toward building a commercially ready humanoid robot, it comes down to two schools of thought:

1. …we could have millions of different types of robots serving unique tasks…

2. …or one humanoid robot with a general interface, serving millions of tasks.

Aside from the grit and will that drives us to do hard things like go to the moon, the biggest argument in favor of a humanoid is that the world we live in is a world built for humans. From door handles to stairs, utensils to hammers, grocery store isles to roads. Sure, we could build a bunch of specialized robots, but rather than adapting the world to robots, the ideal scenario is to build a robot for the human world.

“With one product we can meet the complex human environment with human-like capabilities, and provide endless types of support across a variety of circumstances.”

If a robot has the same characteristics as a human, it can assist us with the tasks that a human might do. It could put dishes away, make the bed and take out the trash, all very dexterous tasks that would require quite a bit of home re-configuration to build a specialized robot for.

And while my understanding of computer vision is securely at n00b level, I'm sure that sharing a form factor with humans would also make it easier to learn the tasks it's meant to perform. If it's programmed to recognize the human form with a head, torso, arms and legs, then it can use computer vision to identify how the arms extend and the fingers grasp, how the knees and waist bend. Being able to show it a video or perform the action in front of the robot and have it learn what needs to happen based on the movements of the thing (us) that looks like itself would be a useful way to extend its capabilities quickly.

As you might imagine, to build such a robot, it needs to engage with the world as a human might. This means it needs legs to walk, fingers to grab things, and eyes to "see" the world around it. When it "sees" something with its digital eyes, it needs to be able to identify it, understand what it's used for, walk over to it with its legs, bend down with its knees, and pick it up with its fingers.

That's a very different set of inputs and outputs than a robot like a Roomba. With a Roomba, it can map your home by rolling around and bumping into walls and furniture. But it doesn't know what those walls and furniture are. It just knows it's in the way. It can't move the object, it can only roll around it. It can't "see" if the floor is dirty or clean, it simply follows a path until it's called back to its dock.

In order to create a general purpose, humanoid robot, there are three big things that we need:

• The Data: We need a large data set to train the neural net on so that it can identify objects in the real world.

• The Brain: We need a powerful neural net to process the training data into a useful digital twin of the real world - or as Kevin Kelly calls it, the Mirrorworld - that the robot can use to navigate.

• The Machine: We need a human-sized, human-weight, human-looking robot with all of the cameras, sensors, batteries, motors, and actuators it needs to provide it with dexterity, strength, precision and movement and the supply chain to manufacture it at commercial scale.

The Humanoid Tech Stack

With the AI boom, companies like NVIDIA have ramped up production of AI training chips, organizations like Hugging Face are encouraging the rapid evolution and distribution of open sourced AI models, and investors are pouring massive amounts of money into the companies who are pushing AI forward. The advancement we've seen in AI has begun to turn the flywheel for innovations in general purpose robots.

But the most clear answer to the question of, "why now?" is because there are companies that finally have all the pieces of the "Humanoid Tech Stack." I believe Google, Meta, Microsoft, and Amazon are all in a position to make progress in this space. However, the company I'm most bullish on is Tesla.

Let’s take a look at the three big things we need to successfully build a humanoid robot - the data, the brain and the machine - and see how Tesla is best poised to “win” this race.

1. Tesla’s Fleet

Tesla's fleet of over 4,000,000 vehicles on the road, each equipped with 9 cameras, are building the largest real-world dataset for computer vision. They're constantly recording, processing and sending video clips back to Tesla's data center for further training.

According to Tesla's website, the training data is processed for semantic segmentation, object detection and monocular depth estimation. Sounds like a bunch of big words so let's break that down:

Semantic Segmentation: The task of classifying each pixel in an image to a specific class. For example, in an image of a street, it would label which pixels belong to the road, which belong to cars, trees, buildings, and so on. The "semantic" part means that it's not just detecting objects, but also understanding the context and assigning a category to each part of the image.

Object Detection: This is the process of identifying and locating objects in an image or video. Object detection algorithms not only classify what objects are present in the image, but also provide a bounding box that indicates where in the image these objects are. For instance, in a picture of a park, object detection might identify and locate a dog, a bench, and a tree

Monocular Depth Estimation: This is a technique used to estimate the depth (or distance) of objects in an image from a single camera or viewpoint (hence "monocular"). In real life, humans use two eyes to perceive depth. But with monocular depth estimation, a neural network can learn to estimate the distance of objects in an image using just one "eye" (or camera).

Essentially, the processing can categorize every pixel, identify and label every object, and estimate their distance in the real world from the camera.

2. Tesla’s Supercomputer

The second piece of the puzzle that Tesla has is their unique ability to process raw data into useful data. For example, Google has an incredibly powerful image recognition engine that they use to power Google Images. But good image processing isn't the same as real-time video processing and analysis.

Tesla, on the other hand, has been doing on-board processing of the footage captured by the 36,000,000+ cameras on production vehicles to enable partial and full self-driving (FSD). That processing has to be so good that it can ingest the footage, analyze it and make a mission-critical, life or death decision in real-time.

The work that Tesla has put into refining and beefing up their processing capabilities translates directly to a general purpose robot's ability to "see" the world and navigate within it, except rather than four wheels, it's moving on two legs.

They recently announced Dojo, Tesla's supercomputer designed specifically for computer vision video processing and recognition. Because Tesla's goal was to process millions of terabytes of video data, they designed a radically different architecture from traditional supercomputers. Dojo will have more than an exaflop of computing power. Let's put that into perspective...

“To match what a one exaFLOP computer system can do in just one second, you’d have to perform one calculation every second for 31,688,765,000 years,”

—Network World (source)

My mind has a hard time wrapping itself around that number... but I know it's a lot!

3. Tesla’s Gigafactory

The last big piece of the "Humanoid Tech Stack" is commercialization. No matter how cool a robot is, the era of every human having their own C-3PO will never arrive if you can't manufacture the robots on the scale of millions of units. While Google, Apple, Meta, Microsoft and others have set up the necessary supply chains and overseas manufacturing infrastructure for products like computers, phones and tablets, Tesla is unique in that it has built its own factories that are designed to build large, mechanically complex robots (eg. electric vehicles). If anyone can adapt and scale existing manufacturing capacity to include humanoid robots, it's Tesla.

In his "Tesla AI Day" event, Elon stated that they're specifically designing Optimus (aka: Tesla Bot) to be manufactured cost-effectively at scale putting the price tag at sub-$20K. It's estimated that 91.7% of people in America own a vehicle. And that vehicle spends most of its time sitting in a garage or in a parking lot rather than doing the one thing that vehicle is built for - driving. If you could get a robot loan for less than a car loan and that robot could do your chores, take care of your pets, maintain your house and yard, and cook your meals while your car continues to sit in the garage, it would be the biggest no-brainer of the century.

The market demand wouldn't be on the order of millions, but billions of units, likely selling multiple robots per person. And that's not including the tens of millions of robots that would be ordered in bulk by corporations for dangerous, labor-intensive jobs.

The Impact of a General Purpose Robot

"As I'm walking about on Main Street, I'll see these - what they call CoCo robots - little 6-wheeled robots that are rolling down the sidewalk delivering six packs of Diet Coke or burgers. And at first they're an oddity when you see them and people are taking photos. And then you ignore them as you're walking by."

—Peter Diamandis

It will feel really odd the first time we see a humanoid robot walking down the frozen section of Walmart. But over time, it will become even stranger to see a human doing their own shopping in Walmart. Not any time soon, but directionally, that’s the future we’re heading toward.

Of course, we're probably still 10 years out from humanoid robots being commercially viable and something like 20 years out from robots in the homes of the early adopters. But with strong company builders like Elon Musk (Tesla) and Brett Adcock (Figure) leading the charge, who knows just how fast we can move and how it will shape our world.

When Karl Benz invented the first vehicle in 1886 and when Henry Ford scaled manufacturing with the moving assembly line in the early 1900's, I don't think anyone anticipated the impact that vehicles would have on the global economy. It unlocked commerce, communication, collaboration and exploration on a scale the world had never seen before. Personally, I believe a general purpose humanoid robot will have a 10x greater impact than the automobile. Here's how Elon explains it...

"The potential really boggles the mind. What is an economy? An economy is capita times productivity per capita. At the point at which there's not a limit on capita, it's not clear what an economy even means at that point. The economy becomes quasi-infinite. Taken to fruition, this means a future of abundance. A future where there is no poverty."

—Elon Musk

Brett Adcock, the founder of Figure, has expressed a similar vision for the future.

“Manual labor could become optional and higher production could bring an abundance of affordable goods and services, creating the potential for more wealth for everyone. We will have the chance to create a future with a significantly higher standard of living, where people can pursue the lives they want.”

—Brett Adcock

Beyond even the economic advantages that robots unlock, there’s an enormous application in exploration as well. Robots can withstand harsher climates, don’t age, don’t need food or water, and can exist outside of the emotional support of community. They can man the mining rigs on asteroids. They can go ahead of us to Mars and begin constructing a settlement. They can be sent in rockets to the farthest reaches of the galaxy. Despite our automated factories, robot vacuum cleaners and the occasional food delivery robot on the streets of San Fransisco, we haven’t yet entered into an age of robotic abundance nor experienced the wealth and prosperity it can unlock.

It's a grandiose vision to be sure. Some of the assumptions that have to be made include:

• Near-limitless, sustainable energy (like Helion)

• Ubiquitous, high-speed internet connectivity (like Starlink)

• Widespread, automated, precision manufacturing at scale (like Hadrian)

• Sustainable and affordable materials needed for batteries (like KoBold Metals)

Despite leaving out a number of very large assumptions in the robot narrative, I love the way that visionaries like Elon and Brett can connect the dots in a simple way to drive home a bigger point. If we can pull it off, a general purpose robot that's built to navigate in a world designed for humans can provide a level of "productivity" that's never been known before. Humans will be freed up to devote their time to creative problem solving, invention, exploration and leisure.

That's the future that Tesla is building.

That’s all for this one - I’ll catch ya next week.

—Jacob ✌️

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This past week, Nature released a paper by a team of researchers from Stanford who developed a way to "rewire cancer drivers to activate apoptosis." If the only word you recognize in that sentence is "cancer," join the club. But thanks to ChatGPT, I was able to break down their research paper into normal people English and learn what I now understand to be a groundbreaking concept in cancer treatment.

Let's start by defining a few terms from that first sentence:

• Cancer drivers are specific genes in our bodies that promote the growth, survival, spreading and resistance to treatment of cancer cells. So these drivers not only help cancer rapidly spread, but also give it the armor it needs to fend off the cancer treatments.

• Apoptosis is a way for organisms (like the human body) to maintain the health of tissues and organs by causing unnecessary cells to self-destruct. You can think of this as biological pruning. In the same way that you would prune a plant to help it flourish, your body prunes its cells.

So if we re-write the title of that paper in normal people English, we'd get something like:

"Researchers from Stanford developed a way to direct cancer cells to self-destruct."

Of course, it's not actually that simple and research is very different from a production-ready therapy but that's the easiest way to think of it. After digging into the science for a few hours, I'm going to do my best to break it down into normal people English below. This article will be a relatively short one, so stay with me!

Let's dive in!

The Cancer (DLBCL)

First, the researches targeted a certain type of cancer that affects the lymphatic system called Diffuse Large B Cell Lymphoma (DLBCL). For the purposes of this article, we'll just refer to it as "Lymphoma." While they tested this therapy across 906 cancer cell lines, the research proved most effective in hematopoietic and lymphoid tissues related to BCL6 expression. What's BCL6 you ask? Great question.

The Target (BCL6)

There's this protein called B-Cell Lymphoma 6 (BCL6) that plays a critical role in the development and function of B-cells. B-cells are a type of white blood cell that helps your body fight infections.

You can think of BCL6 as a Purchase Order that tell the body how many B-cells to produce. So when there's a genetic mutation in the Purchase Order (BCL6), B-cells end up getting produced in higher quantities and this unregulated B-cell growth is what we call Lymphoma.

Then there's this other protein called Bromodomain-containing Protein 4 (BRD4) which is kind of like a Manufacturing Plant. And when it binds with a high concentration of BCL6, its abnormal activity is responsible for activating genes that suppressing apoptosis which, as we now know, is the biological pruning or natural cell death that helps your body stay healthy and balanced. When apoptosis is suppressed, tumor growth goes unchecked.

Somehow, we need to target both the Purchase Order AND the Manufacturing Plant and get them both to calm down.

The Cure (TCIP)

What the researchers developed is this small molecule called a Transcriptional/Epigenetic Chemical Inducers of Proximity (TCIP). Basically it’s a combination of two molecules.

1. The first molecule targets high concentrations of BLC6 like a heat-seeking missile. This is what gives TCIP its specificity so it can target cancer cells but leave healthy cells alone.

2. The second molecule (BI3812) acts kind of like a straight jacket and it can bind itself to the rogue BRD4, disrupting gene activation and allowing apoptosis to do its thing and self-destruct.

So when you release TCIP, the heat-seeking missile molecule targets all of the cells with high BLC6 expressions (eg. Purchase Orders for too many B-cells) and the straight jacket binds itself to the BRD4 so tightly that it disrupts their ability to activate certain genes. Since it's specifically targeting cells with high BCL6 expressions and binding the BRD4, it's particularly effective in cancer cells while leaving the healthy cells alone.

Conclusion

The human body is an complex, beautifully designed organism with the capacity to self-regulate, adapt and heal. But we live in a broken world where things don't always work the way they were designed to. TCIPs are an early step toward correcting some of the "bugs" in our genetic code and restore our body's ability to function as it should. I'm hopeful for a future where cancer is listed alongside polio or smallpox as a tragic piece of our history, but no longer a threat to future generations.

That's the future we’re building.

That’s all for this one - I’ll catch ya next week.

—Jacob ✌️

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I tried to ask ChatGPT to write a short story of what our world would be like with cheap, abundant energy. Unfortunately, it doesn’t have a great imagination since it’s limited to its training data and it got a bunch of stuff wrong. Thankfully, I didn’t have to look very far to find a real person - a qualified person - who already wrote about that future!

“It is not too much to expect that our children will enjoy in their homes electrical energy too cheap to meter, will know of great periodic regional famines in the world only as matters of history, will travel effortlessly over the seas and under them and through the air with a minimum of danger and at great speeds, and will experience a lifespan far longer than ours, as disease yields and man comes to understand what causes him to age. This is the forecast for an age of peace."

— Former Atomic Energy Commission Chairman Lewis Strauss in 1954

This is a future that we’ve come to know only as science fiction. Energy would never be cheap and abundant, right? How could it? I questioned our ability to find near limitless sources of energy within my lifetime until last December when I read about National Ignition Laboratory’s nuclear fusion breakthrough.

I’ve been wanting to write an article on this topic ever since, but I couldn’t find the right angle. I didn’t just want to cover their breakthrough like a news article. I wanted to do a deep dive into the technology itself which, as you might imagine, was incredibly dense. But after doing my homework, I think you’ll enjoy my take on things.

First, I want to set the scene for fusion since folks tend to have a bad taste in their mouth around anything “nuclear.” But then, I want to dive into one of the most ambitious companies attempting to bring commercial fusion to the masses since that’s really the only way Lewis Strauss’ dream will become a reality. You ready?

Put on your hard hat and let’s dive in.

What’s the difference between fusion and fission?

When folks talk about "nuclear energy" they're typically referring to a process called nuclear fission. Fission is the process of firing a neutron into the nucleus of a heavy atom such as Uranium or Plutonium and splitting it into two daughter nuclei. The result is the release of an incredible amount of energy. The first atom bomb used a fission reaction to produce an explosion the equivalent of 20,000 tons of TNT (or 84 Terajoules).

As you might imagine, that scientific breakthrough ignited the imaginations of everyone who dreamed of an energy-abundant future. The energy produced per unit of mass was millions of times more than the energy produced through the traditional combustion of fossil fuels. If we could harness that energy, we would be well on our way to a future where energy was "too cheap to meter."

But of course, the dreams of energy abundance had to be weighed against the risk. After all, the original applications of fission were uncontrolled explosions that decimated the cities of Hiroshima and Nagasaki. Controlling that reaction would be challenging at best and devastating at worst as we saw nearly 20 years later in 1986 with the meltdown of the nuclear reactor in Chernobyl, Ukraine.

While nuclear fission was putting a sour taste in people's mouths around "nuclear energy," fission's more complicated sibling - nuclear fusion - was still trying to find its footing. While fission would fire a neutron into an atom in order to split it, fusion would literally fuse two atoms together under extremely high pressure and temperatures exceeding 100 million degrees. When the two atoms fuse together, the mass of the fused atom is less than the combined mass of the two individual atoms pre-fuse which results in a mass deficit that can be captured as energy.

While fission uses expensive fuel like Uranium and Plutonium, fusion could use less expensive hydrogen isotopes like Deuterium, not only bringing down the cost of the raw materials but effectively eliminating the risk from radioactive byproduct. On top of being less expensive and less risky, nuclear fusion produced ~4x more energy than nuclear fission.

I know this sounds too good to be true and you're probably waiting for the other shoe to drop so let me just cut to the chase. Fusion is friggin hard.

You have to inject your reaction chamber with your fusion fuel in gas form, heat it to around 1,000,000 degrees so the electrons are stripped from the nucleus and it forms a plasma. As you're heating your plasma, you need to use some sort of plasma containment method - we'll explore these more in a minute - in order to keep that million-degree plasma from burning a hole through the side of your reactor. Then, you have to compress the plasma while superheating it to 100,000,000 degrees, fusing the atoms together and producing a new set of atoms that you can then use to capture energy.

It's one big engineering problem with thousands of variables that all have to be dialed in to perfection. And even then, it's not guaranteed that you'll achieve the sort of energy output that you need in order to make fusion commercially viable. But while the challenges ahead are daunting, I'm optimistic. As we continue our deep dive, you'll begin to see why.

A Short History of Fusion

In 1946, President Truman signed the Atomic Energy Act that essentially pivoted the Manhattan Project into the Atomic Energy Commission post-WWII. A classified project within the AEC called Project Sherwood incubated 3 different plasma containment designs.

The first was the Stellarator at the Princeton Plasma Physics Laboratory, the second was the Toroidal Pinch at the Los Alamos National Laboratory, and the third was the Magnetic Mirror at the Livermore National Laboratory. Each of these designs fall under the umbrella of Magnetic Confinement Fusion which uses magnetic fields to contain the white hot plasma.

There's another type called Inertial Confinement Fusion which uses lasers or ion beams to heat up a pellet of fusion fuel which, when it exploded, would use the outward force of the blast to put intense pressure on the fuel within and compress it enough to fuse the atoms. However, the most common version of plasma confinement continues to use magnetic fields.

Fast forward a decade and, in 1958, Soviet scientists created the Tokamak which uses magnetic fields to confine the plasma in a doughnut-shaped configuration that was more efficient, stable, and scalable. After another 10 years of refinement, the Soviets announced that they finally achieved incredibly high temperatures of around 10 million degrees Celsius. The ability to reach these high temperatures in a steady, controlled environment is an important part of achieving a fusion reaction. After announcing their success, fusion teams around the world all honed in on the Tokamak design.

Despite finally creating a stable environment to contain a fusion reaction 500x bigger than the original atomic bomb, we still hadn't cracked the holy grail of net positive energy production, measured by the Q-factor. The Q-factor is the ratio of the energy output to the energy input. If you have a Q-factor of less than 1, that means the energy produced from the reaction is less than the energy required to create the reaction. A Q-factor greater than one means you have created more energy than you spent, something they call ignition.

In December of 2022, we finally achieved ignition for the first time in history. With a Q-factor of 1.5, National Ignition Laboratory (fitting name) proved that they could create a net energy gain. Interestingly, they did so with Inertial Confinement Fusion (ICF) which is a different method than the traditional Tokamak generators which uses Magnetic Confinement.

The Fusion Race 2.0

While clearly a big step forward, experts have stressed time and time again that achieving a Q-factor above 1 doesn't mean it's ready to scale commercially yet. Q-factor can be measured in two different ways: the scientific Q-factor and the engineering Q-factor. The scientific Q measures the thermal energy produced by the plasma against the thermal energy required to heat up the plasma. It doesn't take into account any of the energy required to run the rest of the machine. It's merely measuring the reaction itself. Q engineering, on the other hand, measures the energy produced by the reaction against ALL of the energy used to create that reaction.

National Ignition Laboratory's Q-factor of 1.5 was a scientific Q-factor meaning it wasn't taking into account all of the energy consumed. There are other milestones that must be reached before we can scale fusion commercially such as our ability to efficiently capture the energy that is produced, our ability to capture meaningful net energy production (typically considered to be a Q-factor of 5 to 10).

But don't get me wrong, despite the challenges ahead NIL's work ignited (sorry, pun definitely intended) a new fire for our work on fusion and there are a few dozen startups, each experimenting with their own ideas to bring commercial-scale fusion to life. And there's one in particular that I'm excited about: Helion.

The Shortcomings of the Tokamak

To feel the full weight of Helion's innovation stack, we have to first understand what they're innovating from. The Tokamak generators have been the gold standard of plasma containment methods since the the Soviets achieved 10,000,000-degree temperatures in the 1960's.

The way those generators work is that the Hydrogen ions - Deuterium and Tritium - are used as fuel and heated to extremely high temperatures. As they fuse, the reaction produces a Helium-3 atom and a high-energy neutron. The energy created by the reaction is captured as heat in the walls of the reactor and used to boil water which creates steam. The steam is used to turn a turbine and generate electricity which can then be captured.

Because the Tritium fuel is extremely rare, the brilliant minds at work have figured out a way to create their own Tritium as part of the fusion reaction. When the rogue neutron strikes a Beryllium lining within the wall of the reactor, it splits the Beryllium into a Tritium and a Helium. The Tritium can then be used in the next reaction. While this is a reasonable solution, the neutron that strikes the Beryllium carries 80% of the energy from the initial reaction which means we use 80% of our newly produced energy just to produce more fuel. And unfortunately, because we'd have to use our entire annual supply of Beryllium to create one Tokamak generator, we're effectively trading one rare element (Tritium) for slightly less rare element (Beryllium).

At the end of the day, Tokamak generators will end up going the way of fission reactors - they're too expensive and won't be able to compete with cheaper forms of energy.

That's where Helion comes in.

Introducing Helion

Helion has made a couple of innovations in regards to their fuel that, together, provide the most encouraging path toward an energy abundant future. It promises a cheaper, safer, more efficient fusion reaction.

1. Cheaper

As we've seen, Tokamak machines become expensive due to the Beryllium necessary to produce more Tritium from the fusion reaction. Not only is the Beryllium expensive on its own, it's also expensive to dispose of. Beryllium contains traces of Uranium which, when struck by the high-energy neutrons, becomes slight radioactive. Eventually, the Beryllium lining must be disposed of which incurs an additional cost.

Some companies, like Helion, have opted for a Deuterium-Helium-3 fuel instead of a Deuterium-Tritium fuel which means they don't have to buy the Tritium (expensive) or produce the Tritium with Beryllium (also expensive).

As we've established, Deuterium is incredibly common - it's found abundantly in sea water. Helium-3, on the other hand, is quite rare much like Tritium. In fact, it's so coveted, it's even been suggested that we mine it from the moon - that's how low our supply on earth is. But unlike Tritium, Helion has found a way to produce Helium-3 affordably. They're able to use a fusion reaction similar to how they produce energy, but to create the Helium-3 fuel by fusing two Deuterium together.

Effectively, Helion has found a way to use only Deuterium as their raw fuel source which is ridiculously cheap and abundant.

2. Safer

In both traditional Tokamak reactors and Helion's plasma accelerator, magnetic fields are used to contain the 10,000,000-degree plasma. Neutrons, which are non-charged particles, can slip right through and cause damage to your reactor. They're both dangerous and hard to control.

However, because of their unique fuel combination of Deuterium and Helium-3, Helion's fusion reaction is what's called an aneutronic reaction which means it doesn't release a high-energy neutron. Instead it releases a charged particle in the form of a proton which can be safely wrangled by the magnetic field and, as we'll see in a minute, captured directly as energy.

3. More Efficient

Because the Tokamak designs have to translate a non-charged particle (neutron) into electricity, they end up having to take a less efficient approach by boiling water and using steam to turn a turbine. It's quite inefficient and, as one analyst put it,

"If you think this sounds like a complicated and expensive way to boil water, you’re right."

—Eli Dourado

We can look back at the aneutronic reaction that improved the safety of the reactor as our 2-for-1 solution for efficient energy capture as well! Because fusing a Deuterium and a Helium-3 creates energy in the form of a charged proton instead of a non-charged neutron, Helion is able to use that charge to generate energy immediately without having to boil water.

As the magnetic field of the plasma gets stronger, it pushes back against the magnetic field of the machine causing a change in the machine's magnetic flux. That flux change creates a current in the machine's coils which is directly captured as energy.

Interestingly, not only is it more efficient at energy capture, the Deuterium-Helium-3 reaction produces slightly more energy than the Deuterium-Tritium (18.3 MeV) reaction (17.6 MeV). And, per unit mass of fuel, produces ~4x as much energy as a fission reaction.

Conclusion

Despite solving a bunch of lingering problems with the Tokamak generators, Helion's reactor is not without it's share of challenges. Since the Deuterium-Helium-3 fusion reaction requires that it be heated to 100,000,000 degrees, the engineering challenges ahead will not be easy.

Helion is on their 6th generation reactor (called Trenta) today, and are planning to bring their 7th generation reactor (called Polaris) online in 2024. This 8th generation reactor will be the one to prove it can produce electricity. The primary difference between the two will be the level of engineering required as they attempt to run more frequent cycles, ramp up the power output, capture that energy, turn it into 60hz AC, and pipe it back into the electric grid.

Their goal is nothing short of ambitious - a production-ready fusion plant by 2028. Naturally, it's garnered a lot of skepticism from top minds. But whether or not they hit their timelines, I'm excited to see companies running toward fusion with aggressive, startup-level speed. We need a new generation of companies to take the decades of research from academia and government programs to bring nuclear fusion to commercial scale. Soon, there will be a low-cost, abundant fuel that will transform our world in ways we can't imagine.

That's the future that Helion is building.

That’s all for this one - I’ll catch ya next week.

—Jacob ✌️

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I don't know about you but when I think about "innovation," I tend to get tunnel vision and only focus on technological innovation. Stuff like 3D printing or AI. Hopefully today's article will expand that idea for us all as we explore perhaps the most important innovation of all - one that is a precursor for both the pace of innovation and even the ability to innovate at all. Governance.

"Governance, rules, or institutions are the base layer of society. Businesses and human cooperation are downstream of the legal and governance base layer."

—Niklas Anzinger, Infinita Manifesto

As an American, I will naturally point to our country's birth as one of the greatest examples of innovation in governance. Fighting for their freedom from the rule of Great Britain in the 1770's, the early colonies declared that they would no longer be governed by a monarchy but rather by the people through a new form of governance - a democratic republic. One of the people, by the people and for the people. And while no one can argue our country has had its fair share of failures, that innovation in governance has created the greatest country in history.

But you don't have to look at America alone - Shenzhen, Dubai, Singapore and Hong Kong are all shining examples of the impact of better governance.

I want to unpack what it means to innovate on governance and, were we to treat it as we would technology, how would that shape the way we write and codify laws?

The Long-term Impact of Policy

While I believe politicians will continue to exist in some form even after governance is moved to the blockchain, there are some inherent flaws in the way our current representative form of government works. Primarily, in that those who write the laws are rarely in touch with the areas of life those laws affect. Of course, I realize that there are congressional committees, think tanks, and even the direct voice of the people which (supposedly) serve to provide our representatives with the information they need to write educated and thoughtful legislation. But it's no secret that a lot of legislation gets passed that's... out of touch.

That "out of touch" legislation then get added to the canon of 30,000+ statues that now must be enforced across 330,000,000+ Americans. As you might imagine, in a country with 50 different states with their own sets of laws, nearly 20,000 cities and over 330 million people, there will be unexpected, long-lasting repercussions. This isn't theoretical. This is very real.

Despite rapid improvements in technological innovation thanks to the increasing amount of compute available (Moore's law), innovation in non-technological sectors has stalled and, in some cases, become more difficult. Eroom's Law is the observation that drug discovery is becoming slower and more expensive despite the fact that technology continues to advance. When you look at a chart plotting the rate of drug discovery against the rate of computations, the visual is staggering.

In an ideal world, our law makers are fighting for the good of the people... for the good of the nation. So the obvious question becomes, "how do we ensure we're writing good policies?"

Put another way, "how do we innovate our governance?"

I love the way that Balaji frames our current system for law making by thinking about it from the perspective of someone in the future:

"Our entire antiquated process of adversarially writing high-stakes laws on paper at the last minute, deploying them in production to hundreds of millions of people without any testing, and then getting them interpreted in unpredictable ways by regulators and solicitors will be seen as a bizarre relic of an older time."

—Balaji S. Srinivasan

When you put it like that, it's pretty clear that there's a lot of room for innovation. In the same way that we might run clinical trials or long-term studies for a new drug, we need a better way of testing our legislation in the real-world before it rolls out all 330 million Americans at once.

A Staging Environment for Policy

In computer programming, there are three versions of a piece of software. There's the "production" version which is what everyone interacts with. It's live on the internet or in the app store ready for people to use it. Then there's the "development" version which is the one where you're writing new code. It isn't live to your users yet and only you and your team can see it. There's probably a lot of half-written code throwing dozens of errors every time you tap "run."

And in between those two versions is the "staging" version. The staging version is running the code you *think* is finished but probably isn't. There's bound to be a button that doesn't work, a typo in the headline and a bunch of other bugs that have to get ironed out before you push it live to your users. This "staging" environment is the testing grounds for your code where you can put it in front of real users, take notes, rapidly iterate and even roll back an idea entirely if something isn't as expected.

The next innovation in governance could be something like a staging environment for regulation. A way to test it in a microcosm and iron out the kinks instead of thoughtlessly shipping a buggy piece of legislation to all of your citizens at once. This is particularly useful for highly regulated industries like finance, insurance, healthcare, transportation and manufacturing.

It's obvious that proper legislation makes a tangible difference in an industry. Delaware's c-corp provides flexible corporate governance and attractive tax advantages. Wyoming allows DAOs (decentralized autonomous organizations) to operate in the state as limited liability corporations granting them the rights and protections as traditional business entities. The Catawba Digital Economic Zone in the Carolinas provides a safe and stable environment for technological experimentation thanks to the protections of U.S. treaty obligations.

These are just a few examples of advantageous, localized regulation. And as we study the impact of their unique forms of governance, we can reuse the best of it as needed on a national scale.

Startup Cities as Regulatory Experiments

In the last 4 or 5 years, there has been an explosion of "new cities." The reason I put that in quotes is that there are a few different categories these cities fall under, each with their own twist on the same fundamental idea: to create new forms of governance in the real world.

To use Niklas Anzinger's 1-sentence definition of a startup city...

"[A startup city is] a product with a full stack of a) laws & regulations, b) governance services, c) land & real estate."

—Niklas Anzinger, Introducing the New Cities & Network States Industry Landscape

A city like St. John's Bay, for example, is on the island of Roatan off the coast of Honduras. While it is technically subject to the government of Honduras, it falls within a "Zone for Employment and Economic Development" or ZEDE for short. The cities in these zones are often called "model cities" because the Honduran government provides them with "a high level of autonomy, with its own political system at a judicial, economic, and administrative level." Effectively, its a city-sized country within another country.

St. John's Bay is a city that was created by a startup called Próspera who's goal is to built cities all across the world in these special zones to "maximize generalized prosperity and wealth creation." You can think of St. John's Bay as Próspera's first "product." While it may seem odd to think of cities as products, it does give us a clearer picture of HOW Próspera is thinking about building their cities.

• Great products are crafted based on a combination of quantitative data and qualitative feedback.

• Great products try to solve a user's problem with innovative solutions not just brute force (eg. a car vs. a faster horse).

• Great products try to avoid "feature bloat," simplifying or even removing existing features.

• Great products iterate quickly and try not to stagnate or become comfortable with unsolved pain points.

Rather than political favors, aggressive lobbying and overly-expensive vendors, the principles of great product management, product strategy, and product development become the tools that Próspera will use to innovate at the very base layer of society.

In just the few years that Próspera has been around, they've started 3 different cities and over 100 companies have already begun to explore how their governance innovations can help them, in turn, innovate on their unique technologies.

"Companies are already using Próspera (e.g. Minicircle, Seshat Bank, The Circular Factory, Aerialoop) to reduce their legal fixed costs by 80% and time-to-market by 90%."

—Niklas Anzinger, Infinita Manifesto

Conclusion

These startup cities where new forms of governance are their most valuable export will continue to appear as though we're spinning up a new instance of our staging environment to test a new bit of code. I'm not naive enough to believe that politicians will embrace this idea wholeheartedly, but I'm not defeated enough to give up. Whether or not we partner with Próspera to use their cities as our testing grounds or we develop our own startup cities right here in America, it's an important step toward the innovation of governance.

After a law has been thoughtfully (< emphasis here) written, it should be rolled out to a subset of our citizens first rather than the entire nation. Better yet, rather than testing new policy, we can experiment with removing policy entirely and compare its impact to the rest of the nation. During these testing phases, it will be critical to talk with our "users" in an effort to better understand their needs. Civic researchers will have their hands full collecting and analyzing the data from these experiments.

Finally, we'll be able to test things like universal basic income (UBI) and blockchain-based voting in a real-world setting within America.

I'm not here with answers on how this should be structured. Perhaps the startup cities will be given the option to opt in to piloting certain legislation. Perhaps third-party research centers will conduct the polling and user interviews. Perhaps multiple startup cities will pilot the same legislation in different parts of the country to test for things like cultural, industrial, and geographic variables. Who knows, perhaps all of that will be one big experiment too.

One thing I do know is that this country was founded on the principle that its government would be made up of the people, by the people and for the people. I still believe it's the greatest country on earth, but I haven't felt like our government has been made up of the people, guided by the people or working for the people in a long time. We were the OGs of innovative forms of governance - that's why it gets me excited to think that we could innovate toward a more "user focused" form of policy making. Let's not lose that fighting spirit that believes it can be better.

That's the future that companies like Próspera are building.

That’s all for this one - I’ll catch ya next week.

—Jacob ✌️

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Last week I wrote about Relativity Space, a company that has built the world's largest metal 3D printer and then proceeded to print a rocket ship and launch it into space. If that doesn't get you excited about the future of manufacturing, I don't know what will... oh, except maybe the company I'm telling you about today.

But before we dive into the company itself, it's important that I help you unhinge your mind from the constraints of the world we know today. To do so, I want to start with a question that sounds ridiculous the first time you ask it and then proceeds to blow your mind the second time you ask it.

What if we could grow anything?

No, not just any food... any thing.

• A neighborhood full of organic houses?

• A self-powered spaceship that could take you to Mars?

• A cure for cancer?

• A plastic eater to clean the ocean?

It's the stuff of science fiction... right?

In the last 50 years, the industry of synthetic biology - an industry obsessed with this question - has made rapid advancements toward a "programming language" for biology.

Computer programming started with the 1's and 0's we call binary or machine code and, over many levels of abstraction, has progressed into something very close to the way humans express themselves - "natural language programming" - using A.I. tools like GitHub Copilot. Synthetic biology is working on building similar abstraction layers, giving us the tools we need to quickly and safely program biology.

While not entirely "mainstream," you may have heard of the CRISPR technology from the '80s that introduced the ability to edit DNA. Or perhaps you've heard of "genome sequencing" from the early 2000's in which we created a reference map of the DNA of a human being. These tools were groundbreaking but were the genetic equivalent of writing binary and forking the binary source code of an application. Basically, ground zero.

The next level of abstraction would be something resembling a modern programming language, moving away from the 1's and 0's - or in our case, the A's, T's, G's and C's - and toward the logic of the application.

That's where our story picks up today.

The Father of Synthetic Biology

Back in the day (I couldn't find an exact year) in Wakefield, Massachusetts, there was a young boy by the name of Thomas Knight. We'll just call him Tom. When he was only 14, he started taking courses in computer programming and organic chemistry down the road at a little place called MIT and spending summers working in their artificial intelligence lab.

As you can tell, Tom was... well, the boy was wicked smaht.

He wasn't a janitor at MIT as far as I know, but after eventually graduating, he worked as a research staffer there where he helped develop a computer time-sharing system called ITS, worked on interfacing with ARPANET (the internet before the internet), and helped design the first bitmap-oriented printer.

He went on to complete his Masters and Ph.D programs at MIT as well. And while those would be career highlights for some, they're barely worth mentioning for Tom because during that time, he managed to start a company called Symbolics that would eventually go public (SMBX) in 1984 with over 1,000 employees. An interesting footnote is that the symbolics.com domain was registered on March 15, 1985 making it the first .com domain ever registered. He went on to participate in several startups, some of which would be bought out by the likes of Intel. But I digress.

When Knight started his Ph.D thesis, he began learning how to create integrated circuits (I promise, this is all leading up to something - stick with me) and, while teaching about integrated circuits, he would challenge his students to predict the end of Moore's Law which is "the observation that the number of transistors in an integrated circuit (IC) doubles about every two years." As he began to consider the limitations of semiconductor technology, he began to wonder if biology held the key to more advanced circuitry.

Biological circuitry! Something about that crossover of disciplines struck a chord with Tom and, in 1995, he reached out to DARPA (Defense Advanced Research Projects Agency) to propose a summer study on "cellular computing" which resulted in the creation of the molecular biology lab inside of the computer science department at MIT.

That lab was the birthplace of many key discoveries in the world of synthetic biology.

The DNA Toolkit

As we fast forward a few years to the early 2000's, Tom Knight, along with his MIT colleague Drew Endy, began outlining the standards that needed to exist in the SynBio industry in order to transform it from the wild west to a true engineering discipline.

These standards provide the synthetic biology community with a foundation to build upon without having to reinvent the wheel. Think of it sort of like a coding library - a collection of reusable snippets that can be pieced together according to a set of documentation.

Just like a coding library, synthetic biologists need a collection of reusable biological snippets that were pre-designed and pre-tested so that they could use them with confidence without having to reinvent the wheel. They also needed a set of instructions - like a coding library’s documentation - that tells them how to use these snippets.

And that's exactly what Tom set out to help create.

🦠 Parts Registry

In 2003, Tom was instrumental in the development of the Registry of Standard Biological Parts which is "a collection of genetic elements" such as promoters, reporters, plasmid backbones and terminators that can be combined like Lego bricks to form new biological systems.

The action of consolidating these biological parts into a single registry made it easier for synthetic biologists around the world to discover and reuse what others had already created, dramatically reducing the effort it took to make progress.

🧩 Assembly Standards

The second thing the SynBio community needed was the set of instructions that helped you use these biological building blocks to create something new.

So, along with some colleagues, Tom helped create BioBricks because, as he put it...

"The lack of standardization in assembly techniques for DNA sequences forces each DNA assembly reaction to be both an experimental tool for addressing the current research topic, and an experiment in and of itself. One of our goals is to replace this ad hoc experimental design with a set of standard and reliable engineering mechanisms to remove much of the tedium and surprise during assembly of genetic components into larger systems."

So any biological code snippet from the Registry of Standard Biological Parts that conforms to the BioBrick assembly standard can be used to "design and assemble larger synthetic biological circuits from individual parts and combinations of parts with defined functions, which would then be incorporated into living cells to construct new biological systems."

The creation of the Registry of Standard Biological Parts and the BioBricks assembly standard where incredible steps forward for the world of synthetic biology.

But Tom wasn't done. Oh no, Tom was far from done.

The Platform Company for Synthetic Biology

Cells are super complex organisms not designed by humans containing millions of proteins and synthetic biology is essentially trying to install some "code" in that cell to get it to do something new.

That's a ridiculously difficult engineering problem.

While the parts registry and assembly standards where a revolutionary step forward for the community, it still was still repetitive and expensive for commercial use.

Imagine if you're a company wanting to use a synthetically engineered organism to create a product. Even if you used the parts registry and the assembly standards, you'd end up spending tons of money to spin up an in-house lab and run a bunch of expensive experiments. Probably the same expensive experiments that the company next door is running.

So the question on Tom's mind was, what could make it easier? And it boiled down to two main things that functioned as a flywheel.

1. Cheaper Experiments

2. Consolidated Learnings

Cheaper experiments allowed you to learn faster and consolidated learnings brought down the cost per experiment.

So imagine if your experiments could be done at scale, automated by computers and robots that you didn't have to pay for. And imagine if those learnings could be codified and tested at scale. You could not only run your experiments more affordably but build off the learnings from previous experiments before you. This is a classic example of the adage, "a rising tide lifts all boats."

That's where Ginkgo Bioworks comes in.

"Ginkgo's made the choice that we would rather have broad exposure to the entire industry. And we win with the winners and when products on our platform fail to get to market because the clinical trials fail to work so the science doesn't work, no problem. We generated interesting reusable learnings, and we'll dedicate that capacity to the next thing. We do not live or die on any given program."

—Anne Wagner, Ginkgo SVP

In 2008, a group of scientists, Jason Kelly, Reshma Shetty, Barry Canton, and Austin Che who were working with Tom and Drew in the molecular biology lab, had recently graduated. Together with Tom, they decided to start a company called Ginkgo Bioworks in an effort to move away from the academic pursuit of synthetic biology and toward real-world applications.

You can think of it as the AWS of SynBio. If you are a software company who needs servers, you don't want to have to set up your own server infrastructure, you'd just want to pay AWS and get scalable servers on demand. Ginkgo was the first platform company for synthetic biology, offering a "lab as a service" for companies who needed biological organisms without the CapEx of building their own lab in-house.

With 250,000 sqft of highly automated labs across 4 “biofoundries," Ginkgo can reduce the man hours and the cost of running an experiment, building upon the learnings of millions of automated experiments.

"Ginkgo Bioworks uses Python code to pull out DNA sequences that might produce a gene to make a desired product, like a specific fragrance. Robots are used to print out and assemble the DNA sequences and insert them into bacteria or yeast. The bacteria and yeast strains are tested for fitness, efficiency, and yield. Data from this process are captured and used to build design principles to make the future engineering of organisms more efficient. With automated robots, one Ginkgo operator can perform around 1,000 DNA extractions in a day."

—Golden

So what?

This all sounds nice, but how does it impact our day to day life? Elliot Hershberg, the author of Century of Biology (highly recommend!), recently attended the summit in Zuzalu. Here are a few takeaways that he mentioned in his article last month:

Several incredible examples were on display at the summit in Zuzalu:

Materials: Biomason CEO Ginger Dosier shared her vision to build a distributed network of bioreactors that grow cement using biology. Cement is the world’s most used human-made material, so the transition away from kilns would have a huge impact on reducing carbon emissions. Clearly, distributed cement production would be interesting for network states.

Chemicals: Jennifer Holmgren, the CEO of Lanzatech, told us about the synbio-powered chemical plants they are building around the world to convert carbon emissions into products like fuels, proteins, and materials. They feed emission streams to microbes that spit out chemical products using their metabolic machinery. Could network states one day crowd-source ownership of their own chemical plants?

Medicines: synthetic biology isn’t just applicable to planetary health—it also has the potential to transform human health. Mark Kotter, who is an academic neurosurgeon and the CEO of bit.bio, told us about his vision to develop a new generation of medical therapies by reprogramming human cells. Specifically, bit.bio is building a platform on top of induced pluripotent stem cells (iPSCs), another Nobel-winning discovery that makes it possible to transform differentiated cells back into stem cells—the type of cell that can then be transformed into any other cell type in the body. This vision directly overlaps with the longevity focus at Zuzalu.

Food: Agriculture is a critical module in the operating system of any modern civilization. Jason Kelly highlighted several key strategic acquisitions that dramatically expand Ginkgo’s capabilities in this market. Mark Kotter also highlighted the potential for the bit.bio platform to accelerate the growing cellular agriculture market. So far, removing animals from food supply chains has been an afterthought for industrial economies with entrenched processes. For new societies without almost 100 million cows, this value proposition could be a lot more compelling.

—Elliot Hershberg, Blockchain Meets Bio

These examples are from the SynBio community at large, but Ginkgo has been equally as busy, partnering with companies across a wide variety of industries to use biology to develop better products for the future.

• Living medicine: They're helping Synlogic develop living medicines that can dynamically respond to diseases, break down toxic molecules, deliver necessary enzymes, and send up signal flares to help the body discover harmful tumors.

• Bio-based fuel: They're working with Visolis to engineer a microbial strain for the production of bio-based isoprene and aviation fuels.

• Healthier crops: They're working with Syngenta to design seeds for healthier and more resilient crops.

• Natural baby formula: They're working with NAMUH (human spelled backwards) to develop functional oligosaccharides that are structurally identical to those found in human breast milk to create infant formula comparable to human breast milk.

These partnerships are just a handful announced in 2023 alone but it gives us a really interesting picture of just how many things in the world biology can revolutionize. While a majority of products fall into one of four buckets - drugs, materials, foods and chemicals - McKinsey estimated that "as much as 60 percent of the physical inputs to the global economy could, in principle, be produced biologically."

The scope is too broad to really wrap my head around, but the more I think about the world of SynBio, the more I begin to see just how impactful it can be. I want to zoom out a bit, beyond out city, our country... even our planet, and imagine our future colonization of Mars. While companies like Relativity Space are building metal 3D printers to produce parts and tools to help us construct our Martian cities, companies like Ginkgo may one day send a biofoundry to space so we can develop new organisms to help us terraform the Martian surface.

Perhaps we could create an atmosphere on Mars to protect us from the sun's heat. Or transform its dusty surface into fields of lush vegetation. Or create a chemical plant to grow rocket fuel that will power future space missions.

That's the future Ginkgo Bioworks is building.

I know, I know… I’m getting ahead of myself but I can’t help but dream about the possibilities. In the meantime, we literally growing cement, fuel, medicine and meat and that’s enough to blow my mind :)

That’s all for this one - I’ll catch ya next week.

—Jacob ✌️

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Just 3 months ago (on my birthday), a company called Relativity Space launched its first rocket into space. This sort of thing isn't a novel occurrence these days with SpaceX launching multiple rockets every single week. But what made this particular rocket launch special was the fact that this rocket was the first 3D printed rocket ever. If you're wondering if you read that correctly, believe me, I get it. When I think of 3D printed things, they're usually small and made of plastic. Not something I'd consider launching into outer space.

But let's back up because the story of the founders of Relativity Space, this guy named Tim Ellis and his college buddy, Jordan Noone, is a rather interesting one. Tim and Jordan met at the University of Southern California at the USC Rocket Propulsion Lab, a student club that became the first student group in history to launch a rocket into space. And while Tim and Jordan weren't students at USC for that historic moment in 2019, they paved the way for it years earlier when Jordan became the first student ever to receive FAA approval to launch a rocket.

After college, Tim went on to work at Blue Origin, the space company started by Amazon founder, Jeff Bezos, while Jordan took a job down the road at SpaceX. In beautiful foreshadowing, while at Blue Origin, Tim started a proof-of-concept metal 3D printing division that he believed could revolutionize the way rockets were designed and built. Unfortunately, Blue Origin was less interested in disrupting rocket part supply chains so they never really gave Tim a chance to build it out. Undeterred, Tim teamed up with Jordan and started a scrappy little space startup called Relativity Space with the mission of 3D printing an entire rocket. Casual.

Origin story of Relativity

As you might imagine, you couldn't pick up a metal 3D printer that could print rocket parts from your local Best Buy, so Tim and Jordan not only had to figure out how to build a viable rocket... they had to build the machine that would build the rocket.

Even they realized that it seemed like a step backwards but they believed that given enough time, they could surpass traditional manufacturing and supply chains after they got over the technical hump.

"[The company was] founded on the premise that additive manufacturing, alongside artificial intelligence and autonomous robotics, will deliver eventual superiority over traditional aerospace manufacturing methods."

—Tim Ellis, Space.com

Once they got over that hump, it would unlock the ability to re-think rocket designs, reduce points of failure, iterate more rapidly and reduce reliance on third party vendors. Though daunting, the more the pair talked about this vision, the more convinced they became that it needed to exist.

3D printing the rocket provided a laundry list of benefits:

1. Simplified Supply Chain: Perhaps the most obvious benefit is the simplified supply chain. If you're sourcing parts from even a dozen different companies, you're reliant on each of them to deliver. Even if one part isn't available or is delayed, you're SOL.

2. Faster Iteration: In traditional manufacturing, the tooling needed to produce a part has to be set up. And because of the time invested in setting up the tooling, the manufacturers want to create a bunch of the same parts before repurposing the machines to create something else. So creating a one-off part is cost prohibitive. 3D printing provides the flexibility and "one size fits all" approach to manufacturing allowing for rapid iteration, experimentation and manufacturing.

3. Higher Flexibility: Because some parts are standardized in traditional rocket design, your design inherently has constraints. But if you could adapt any part to fit your needs, you could get creative with the rocket design.

4. Cheaper Labor: Even with human-assisted manufacturing such as Hadrian requires a highly skilled labor force. But because 3D printers require fewer people to operate them, you require fewer workers leading to lower labor costs and cheaper parts in the long-run.

5. Fewer Points of Failure: Because 3D printing is an additive manufacturing approach versus the traditional subtractive manufacturing that cuts away material, you can combine parts into a single unit reducing the overall number of parts which reduces the points of failure.

A sequence of “firsts”

Relativity was founded in 2015 and just a year later in the Summer of 2016, they successfully tested their first rocket engine called the Aeon 1. This was a special milestone for the company and for the industry because it was the first engine ever to be 3D printed, proving the viability of the company's ambitious mission.

Of course, an engine is only one part of a rocket. They still had a long way to go before they could send a rocket into space. Let's fast forward 7 years to March 22, 2023 when Relativity launched their very first rocket, the Terran 1.

"Relativity Space's first rocket, the Terran 1, is the world's first 3D-printed rocket. Its first flight — an expendable test launch dubbed "Good Luck, Have Fun" — lifted off from Florida's Cape Canaveral Space Force Station on March 22, 2023. The rocket performed well for the duration of the first-stage burn and went through a successful stage separation, but an issue with the second stage meant the historic flight failed to reach orbit."

—Space.com

About 85% of the Terran 1 was 3D printed which made it the world's first 3D-printed rocket to pass the Kerman Line (100km altitude) and the first 3D-printed rocket to survive Max Q which is the maximum amount of stress on a rocket. Even though there was an issue in the 2nd stage that caused it to not reach orbit, the team was able to glean a lot of interesting data to use as they iterate on their second and larger rocket, the Terran R, which they are aiming to be 95% 3D printed.

"No one's ever attempted to launch a 3D-printed rocket into orbit, and, while we didn't make it all the way today, we gathered enough data to show that flying 3D-printed rockets is viable,"

—Arwa Tizani Kelly, Technical PM at Relativity Space

The other interesting milestone of this launch was the fact that they used a methalox (liquid oxygen × methane) fuel. Though they aren't the first rocket in the world to do so, they are the first in the West, beating out SpaceX's Starship by a month.

While this milestone is incredibly exciting, two questions came to my mind when reading about their recent launch.

• Why do they care that the next rocket is 95% 3D printed? Why isn't 85% good enough? Or 50% for that matter!

• What's the significance of using a methalox fuel? Why not use rocket grade kerosene (RP-1) like SpaceX's Falcon 9?

After diving down that rabbit hole, I stumbled upon Relativity Space's grand vision. I was under the impression that 3D printing a rocket was a pretty ambitious vision already but as it turns out, that was just Step 1 🤯

Relativity Space's Grand Vision

When Relativity Space first got started, there were a lot of haters, as expected. Traditional manufacturing has had 200+ years to work out the kinks of creating precision parts en masse. Additive manufacturing on the other hand, was a relatively new method with less than 50 years under its belt. There were a lot of challenges to overcome and it was easy for people who couldn't see the bigger vision to quip, "you can already buy that part super cheap from an existing supplier, why are you re-inventing the wheel?"

And I think in the early days that criticism was understandable. Because Relativity did have to invest time and money to build the world's largest metal 3D printer (which they dubbed Stargate). And they did print a lot of parts that they could have just bought off the shelf. But with every part they printed and every upgrade they made to the printer, Stargate became more powerful and the parts became cheaper to produce.

"Stargate is a metal 3D printer for aerospace applications. It allows Relativity Space to build a rocket in 60 days, which will allow production to take place without involving complex supply chains and, according to the company, reduce part counts by 100x. [...] Historically, the production process for rockets was extremely long and complicated, taking up to two years due to the coordination required with numerous suppliers."

—Contrary Research

Now they can print a rocket in 60 days instead of the traditional 2 years while simultaneously reducing the part count by 100x. Impressive! But they weren't done. While 3D printing their own parts was a brilliant competitive advantage from a cost and risk management perspective, for Relativity it was more of a proof of concept.

• They used machine learning to understand the makeup of the materials they were printing with and then...

• they invented new alloys specifically designed for Stargate to meet mission-critical performance standards and then...

• they used a combination of additive and subtractive manufacturing to produce parts with complex geometries which...

• they tested using computer vision for real-time quality control and part inspection.

In case it wasn't obvious already, Stargate isn't your run-of-the-mill 3D printer. So let's get to the juicy part - the part that explains why it's all worth it.

“If we’re going to exist on Mars, there has to be a factory that’s small and lightweight, that can build products with not a lot of human labor, in very difficult conditions,” Ellis says. “I thought all of that described an intelligent metal, 3D-printing-based factory.”

—Tim Ellis, Fast Company

There it is. A factory on Mars.

That's the bigger vision. And that's why Relativity is using methalox fuel - because it’s made from two resources that can be found on Mars. It’s also why they’re working so hard to create a 3D printer that can analyze materials to understand their unique properties, get creative with part design based on those properties and 3D print 100% of something. Because if you're going to build a factory on Mars, you don't have the luxury of waiting around for:

• a large quantity of materials to print with or...

• a bunch of materials scientists to come analyze the local materials or…

• a large quantity of highly-skilled workers to operate the factory.

So the factory needs to be highly adept at using local materials in new, creative ways with a skeleton crew.

Elon has predicted that for a Mars to be a fully functional and thriving settlement, we'd need no less than 1,000,000 people.

“Even at a million, you’re really assuming an incredible amount of productivity per person, because you would need to recreate the entire industrial base on Mars. You would need to mine and refine all of these different materials, in a much more difficult environment than Earth. There would be no trees growing. There would be no oxygen or nitrogen that are just there. No oil.”

—Elon Musk, interview with Aeon Magazine

To support a million people, we need infrastructure. And to build infrastructure we need the survival basics like shelter, heat, food and water. And to build our survival basics, we need tools and parts.

Elon’s plan is to shuttle tools and parts to Mars via resupply missions in between the thousands of human shuttles bringing people to Mars. But… what if you could just make your parts and tools there?

Relativity is on a mission to build our industrial base on Mars, from which we could build habitats, rovers, energy plants, food production facilities and water reclamation systems. Slowly but surely, we could begin to establish and grow our small Martian base into a thriving Martian colony.

Relativity's flagship product isn't their 3D printed rocket... it's their 3D printer.

Their small, lightweight, adaptive Martian factory.

That's the future Relativity Space is building.

But I'm curious to hear your thoughts!

• Is 3D printing going to take over traditional manufacturing in the future?

• Do you think this is the best option of expediting Mars colonization?

• How soon before SpaceX buys them out? 😂

Leave a comment

That’s all for this one - I’ll catch ya next week.

—Jacob ✌️

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Yesterday, Apple released the all new, Apple Vision Pro. It's their mixed-reality headset competing with Meta's Quest and Microsoft's HoloLens.

As expected, Apple packed it with tons of "Apple-esque" features like:

• auto-screencasting for your Mac

• eye tracking (point) and hand gestures (click) for navigation and interaction

• 3D video capture and immersive replay

Community: what’s your favorite Vision Pro feature? Drop it in the comments!

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I considered writing about all of the features and specs but I know there will be many other writers covering those in detail so I want to zoom out and attempt to answer two bigger and more complicated questions.

Why did Apple build a headset and is the world ready for it?

This question is particularly potent considering that Meta has (very) recently lost $13.7B on their investments in the Quest headsets and have since pivoted their focus from the Metaverse to AI after mounting pressure from shareholders.

Let's get into it.

Why a headset?

The Vision Pro is Apple's first new product category since HomePod five years ago in 2018. So why this? Why not a car, or a camera, or a home security system? Surely, given the constant rejection of headsets, this is NOT the play... right?

To answer this question, I think we have to look at the trajectory of computing.

Over the last 50 years, we've evolved from room-sized computers to a desktop computer at the office to a laptop in our home, to a phone in our pocket, to a watch on our wrist, to an earbud in our ears. Each step bringing technology closer to us in a more personal, more powerful way. And a headset is the natural evolution of computing, bringing the screen from our hands to our face. Bringing the digital world from our screens into our real world.

Some, like Humane, have argued that the next computing platform is wearable AI. Screen-less and almost invisible. They're moving away from the visual aspects of computing in an attempt to help us engage more deeply with the world around us.

An admirable mission to be sure, but I believe, doomed to fail. We won't let go of our Instagrams and our TikToks so easily, will we?

But they have gotten one thing right... whatever we build next should be a shift away from the glowing portals of our screens. Something that allows us to swing the pendulum back toward the real world. And while there's something romantic about Humane's vision of a world without screens, I'm afraid they've swung the pendulum too far too soon.

Perhaps one day in the future we'll break our reliance on screens altogether, though I tend to think probably not. Until that day comes (if it comes), there's a better option. A middle ground that begins to shift us back toward a more present IRL experience without taking away our precious pictures. A digital layer superimposed onto our world. Even in the fictional world of Marvel, Iron Man's J.A.R.V.I.S. projected holographic visuals that Tony Stark could interact with. Audio alone isn't enough - we're sensory beings who like to see and touch.

A headset fits in the in-between. A space between one end of the pendulum with its laptops and phones and the other end with its screenless, ever-present AI. It's the next computing platform. The next evolution of our parallel world.

I believe that's the "why" for Apple, but of course, that begs a broader question. Is the world ready for it?

Are we ready for it?

I'll be the first to admit that I was both excited and shocked when the rumors began surfacing that Apple was going to announce a headset at WWDC. Excited because, as a regular Quest user, I've been looking forward to the day when Apple would release a competing product that would pair with the rest of my devices, sync with iCloud and provide a more immersive, higher resolution experience. And shocked because the world seemed to have all but rejected the headset.

Virtual Reality has gone through multiple hype cycles and intense skepticism. And Augmented Reality's spark seemed to died out into nothingness with Google Glass. When Meta bought Oculus and began to invest in the games and worlds of the Metaverse, I was genuinely hopeful that it would make a comeback. I even wrote an article about why I thought Meta could turn around the public perception, but alas, I watched it get rejected once again by the fickle masses.

So yeah. I was a bit confused when Tim Cook began teasing his love for AR in interviews. But when I looked through my Apple-colored lenses (pun intended), things began to make more sense. Despite Meta's attempt and debatable mainstream "failure" with the Quest, there are a few things Apple has going for them that gives them a leg up.

Let's break 'em down.

Apple's Secret Sauce

1. App Store

Use case has always been an uphill battle when inventing a new modality. Back during the Personal Computing Era, they gave people a spreadsheet and eventually a word processor and then email - the use cases came much more slowly.

During the Mobile Computing Era it was a bit easier since the iPhone replaced something consumers already used - a phone. But with the introduction of the App Store, suddenly a million use cases were at their fingertips.

In the Spacial Computing Era, you once again, have to give the people something to do. A use case that justifies a $3,499 price tag. This was part of the downfall of Google Glass - for the price, it just didn't do anything your phone couldn't. An app store is table stakes of course, which is exactly what Meta attempted to do but without a strong community of developers building for Quest, the every-day use cases just haven't added up quick enough. The Meta Quest Store only has 1,000+ apps.

Apple's App Store on the other hand is already seeded with 14,000+ AR apps that their customers have been using on their iPhones and iPads for years. Plus, you can run your non-AR apps as 2D floating screens within your H.U.D. offering millions of use cases that consumers are already reliant on. Not to mention, Apple's partnership with Disney brings an entire world of entertainment options that you've never seen before like under the sea excursions and MBA games rendered on your tabletop in a stunning 3D miniature model.

2. Developer Ecosystem

Speaking of use cases, how do you generate new ones? That brings us to the developer ecosystem. If you're opening up your new computing platform for folks to build for, they obviously will want to know there's a distribution advantage that's worth their time. Since augmented/virtual reality is a new platform, you have to give the builders a reason to build.

Since Meta's apps are relegated to the VR world that Meta built, developers are relying on Meta to build the hype for Quest so consumers buy the headset so there are people who will actually use their app or play their game. As we've mentioned, Apple owns two of the most ubiquitous devices in the world (iPhone/iPad) that already supports AR apps without the headset. That boosts app distribution, encouraging more developer engagement and spins the consumer flywheel.

3. Device Interoperability

Speaking of the iPhone/iPad, let's talk about device ecosystem. Apple is world-class at building a walled garden of connectivity. Within their garden, their devices seamlessly work together creating an almost magical experience. And the Vision Pro is no different. From the moment you put on your headset, your photos, notes, contacts, passwords etc. are piped in and ready for you to use just as you would expect.

The inability to easily sync your digital world into your new "space" makes using the Quest a bit of a cold start while Apple makes your first experience in their spacial world feel like home.

The Metaverse

Now, let's talk about what I believe is the biggest advantage Apple has going for it. While Meta focused on the VR-first play (eg. The Metaverse), Apple is focusing on an AR-first play (eg. The Mirrorworld). Rather than escaping reality, Apple wants to enhance it.

Before we jump into that, I want to first give some kudos to Meta because, while it may have been a misstep, it wasn't an obvious one in my opinion. The idea of the Metaverse is a grand one. It promises endless worlds to explore, games that defy the laws of physics and an immersive landscape that creates a breathtaking backdrop for work and play. I get it - I really do. There's an inescapable allure to the Metaverse.

But while Meta's pursuit of VR was admirable, the tech simply wasn't good enough to bring the vision to life and, to some degree, I just don't think the world was ready for it. Folks have been talking about the addictive, anti-social and depressive qualities of looking at screens for years now. If we're honest, strapping it to your face and blocking out every speck of the real world was a non-starter.

That being said, there is a definitive "first step" toward virtual reality and that's augmented reality. For a computer on your face to run, it first has to walk. But thankfully, AR is an end game all to itself. How many of us have watched in awe as films like Iron Man and Minority Report depicted high-tech, holographic desks or H.U.D.s?

In terms of the cultural zeitgeist, AR is a better representation of "the future" than VR which most often is associated with a dystopian world (à la Wall-E). So naturally, when Apple decides to build version zero of the next great computing platform, they're going to build something that feels like a dream, not a nightmare.

The Mirrorworld

With all of that as context, I want to visit an idea that legendary futurist, Kevin Kelly, wrote about back in 2019. The Mirrorworld.

Despite the incredible technological innovations that Apple brought to this new headset modality, there's still a long way to go. I got hyped about the super high-def resolution and impressive hand tracking, but a $3,499 headset with a short 2-hour battery life and that creepy "Eyesight" passthrough thing is very much version zero.

But that doesn't phase me in the least because, with the help of Kevin Kelly, I can see (pun intended) the future that Apple is building toward. Here's how Kevin describes The Mirrorworld.

"At first, the mirrorworld will appear to us as a high-resolution stratum of information overlaying the real world. We might see a virtual name tag hovering in front of people we previously met. Perhaps a blue arrow showing us the right place to turn a corner. Or helpful annotations anchored to places of interest. (Unlike the dark, closed goggles of VR, AR glasses use see-through technology to insert virtual apparitions into the real world.)

Eventually we’ll be able to search physical space as we might search a text—“find me all the places where a park bench faces sunrise along a river.” We will hyperlink objects into a network of the physical, just as the web hyperlinked words, producing marvelous benefits and new products.

These examples are trivial and elementary, equivalent to our earliest, lame guesses of what the internet would be, just after it was born—fledgling Compu­Serve, early AOL. The real value of this work will emerge from the trillion unexpected combinations of all these primitive elements."

—Kevin Kelly, AR will spark the next big tech platform - call it Mirrorworld

The Mirrorworld that was teased in its infancy with Google Glass, has begun to evolve and take on the form and function of our personal and mobile computing devices with Apple's Vision Pro. It looks new and exciting but also familiar with the array of icons denoting our messages and photos. Except now, we see some of those things appearing as digital objects, inserted into the real world around us. An x-ray view of a car letting you see the inter-workings of the engine. A 3D heart. A digital turntable.

As the technology gets smaller, lighter and cheaper to produce, heavy aluminum goggles with an external battery pack will turn into slimmer, lighter, stand-alone goggles. And those will, in time, turn into glasses that feel natural, though perhaps a little bulkier than usual. And maybe someday, when over-the-air charging, holographic technology, and chip production has reached an unimaginable maturity, we'll all be wearing glasses that look and feel as natural as they do today, casting an ever-present digital overlay on the world around us.

When that day comes, a bulky laptop that you have carry around in a backpack will feel like a relic and a phone in our pockets might even feel unnecessary.

That's the future Apple is building.

But I'm curious to hear your thoughts!

• What’s your favorite feature of the Vision Pro?

• Do you think it has what it takes to go mainstream?

• What do they have that the Quest doesn’t and vice versa?

Leave a comment

That’s all for this one - I’ll catch ya next week.

—Jacob ✌️

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The world is changing fast, industries are being disrupted and yet somehow, there are still areas that are stuck in tradition. Legacy that won't be overwritten.

The education system, the medical system... and housing.

According to habitat.org's 2022 State of the Nation’s Housing Report,

"The U.S. now has a deficit of 3.8 million homes, with the greatest supply shortages at low-income price points. The backlog remains large enough that it could take a decade of record-level homebuilding to meaningfully increase affordability."

—2022 State of the Nation’s Housing Report

Even if you've never had a home built before, if I asked you to list some of the problems with the traditional home building process, I'm sure you could find a few.

• It's hard to estimate timelines. Home construction takes 8 months on average, but regularly taking more than a year, blowing through estimated timelines. Do you know of a single home construction project that finished on time? I didn't think so.

• It's hard to price. Costs often run over on traditional construction projects and it's difficult to know exactly how much you're going to end up paying, especially with material costs such as lumber fluctuating as they do. Do you know of a single home construction project that came in under-budget? I didn't think so.

• It's inflexible. Want to hang a picture? Punch a hole in the wall. Want to fix a leaky pipe? Punch a hole in the wall. Want to add another room? Punch a hole in the wall. The process of "editing" your home is tedious, messy, and destructive by nature.

The solution to some of these problems seems to be pre-fab homes. You can build them in a warehouse that's designed for maximum work efficiency, with all of the machines and tools you need easily accessible, and you don't have to worry about weather delays. Build from a list of pre-designed layouts and boom, that should solve the timeline and price issues, right?

By now, I'm sure you've already spotted the downfalls of pre-fab. You have transportation logistics to deal with and, more importantly, you're still building with traditional materials like wood framing and sheetrock siding!

That's where this company called Cover comes in. It takes a unique approach to home building that’s technically still pre-fab but, other than that, it's completely different.

Cover is attempting to tackle the efficient manufacturing of homes using assembly lines and robots instead of construction workers and power tools while solving for the very obvious problem of customizability.

Let's break it down.

Cover’s Solution: The Innovation Stack

Jim McKelvey, co-founder of Square, wrote about this idea called The Innovation Stack which says that, rather than creating one massive innovation, a company can stack a bunch of smaller innovations to both build a moat and add differentiated value.

Square's first small innovation was this idea of the "flat-rate fee". Up until Square, transaction fees were complicated and expensive. The flat-rate fee simplified transaction fees for the merchant and made it simple to estimate their expenses.

But of course, a flat-rate fee is only profitable at scale so they had to incentivize adoption among the millions of smaller merchants they were trying to serve. These merchants couldn't afford expensive terminals but many had smart phones which were becoming more ubiquitous, so Square built a card reader that would plug into the headphone jack on the merchant’s phone. Of course, the Square reader is the innovation that they're known for today, but it was born in pursuit of a smaller innovation.

Cover's approach is similar.

Their original mission was to help solve the affordable housing crisis by making home building faster and more efficient which could be accomplished a dozen different ways.

Innovation #1: Assembly Line Manufacturing

But if we learned anything from Henry Ford, we know that the best way to improve speed and efficiency is through an assembly line. So they set out to build homes using the manufacturing process.

Innovation #2: — The Panel

Assembly lines unfortunately reward standardization and homes are incredibly personal and unique. The number of rooms, types of rooms, layout of rooms... it varies from person to person. So Cover decided to innovate smaller. Think of Legos. The atomic unit (the Lego brick) can be standardized and mass manufactured, leaving infinite room for customizability of the final product.

But what IS the atomic unit of a house? Is it as big as a room? Or as small as a screw? Cover landed on the idea of a wall panel as the atomic unit. You could manufacture a panel with support for insulation, plumbing, and electrical. You could build panel covers that easily pop on and off that make it simple to work behind it or even "paint" your house by swapping out the panel covers. Then, of course, you could arrange the panels in infinite combinations and orientations like Lego bricks providing plenty of room for customizability.

Innovation #3: Unique Materials

If you want to make the panels easy to manufacture, transport, update and snap together, you probably want to shy away from wood and sheetrock. We’re trying to avoid busting holes in our walls if we can help it. So Cover researched the perfect materials to make their panels with.

"The materials it is using are lightweight steel for the building frame and aluminum for the ceilings. The panels are made of a rubber composite because, as founder and CEO Alexis Xavier Rivas explains, 'drywall is not designed for manufacturing or transport — it’s too brittle.'"

Innovation #4 — Software Assist

Just because these panels are the atomic units doesn't mean that you want to just sell them at Home Depot and call it a day. There's still a lot of work and thought that goes into building a home with them. The site prep, the permitting, the installation... it's still quite complex. So Cover needed to offer a service to their customers that abstracted away all of that complexity and makes it simple for the homeowner to pull the trigger.

Like manufacturing, software is great at turning human labor into automated processes. So Cover built a tool that helps its customers design their homes. This had two massive advantages:

First, because software is great with data, they can use geo-spatial and zoning data based on the customer's address to capture all of the information necessary to submit the permit application to the local officials.

And second, the customer's design tells Cover exactly how many panels to produce which they know the cost of down to the bolt. So the software can give the customers an immediate and accurate price.

Cover’s Approach: The Master Plan

As I started digging into the research around Cover, I started seeing a bunch of comments around price. Which, honestly, is a fair assessment at first glance. Cover's price tag ranges from $250K to $500K for a sub-1,200 sqft home. You could hire a GC to build the same sized home for half the price with the traditional construction method, which brings up a second criticism - the size.

Cover has marketed themselves as a "backyard home" and, while you could certainly live in one, they're most often used as mother-in-law suites, guest homes, rentals or backyard offices.

So it begs the question, if Cover's mission was to disrupt traditional building practices in order to help address the affordable housing shortage, have they not failed? There's no world in which $500K backyard homes would be considered "affordable" or "housing." To answer that question, we have to look at the playbook of another pioneer in the tech space: Tesla.

Tesla's now-famous Master Plan outlines their strategy to build an electric car company in a world where electric cars were only purchased by super-nerds and car enthusiasts. Since that was the entirety of the market back then, that's who they catered to.

They designed a powerful, beautiful electric sports car called the Roadster and then used the proceeds of those sales to build out the manufacturing infrastructure necessary to produce a cheaper car. The Model S. They repeated this playbook a few more times until they could offer a reasonable, competitive price for a modern, electric car that the Average Joe might buy.

Cover ran into a similar problem: how do you scale manufacturing infrastructure without billions in the bank? They already had the answer.

Build a beautiful, high-end option for a smaller, price-insensitive audience.

Fast forward

That's where Cover is today, but as they've stated publicly, they're not slowing down. Their eventual goal is to bring costs and install times down, build out more manufacturing plants, and expand into multi-story and multi-family homes. As the CEO of Cover remarked, it's just a matter of using 10,000 panels instead of 100.

Since starting Cover six years ago, the team has managed to design their modular panels, set up the manufacturing infrastructure to begin production, start building homes and bring install timelines down from 120 days to 30 days.

Today, if you pit a gas car against a Tesla of the same price, you'll likely choose the Tesla because it's faster, smoother, quieter, and cleaner. In the future, if you pit a traditional home against a Cover of the same price, you'll likely choose the Cover because it's easier to price, install, maintain, extend, and redesign.

My final thought on the matter is around design. As a minimalist and designer myself, I think their homes are beautiful and sleek. But they do tend to lack a certain soul. But I think, again, even that criticism is too short-sighted. Cover's priority right now is the infrastructure for production. If they can't get that right, it doesn't matter how soulful their homes are, they're dead in the water.

I'm confident that, as they begin to scale production, they'll find ways to deviate from the standardization that makes Covers simple to produce and introduce a bit of character into their designs.

But I'm curious to hear your thoughts.

• Do you think Cover’s approach will take off?

• Do you like their home designs?

• Who else is addressing the home shortage in a creative way?

Leave a comment

That’s all for this one - I’ll catch ya next week.

—Jacob ✌️

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This article is Part II of a 2-part series on Reflect. Part I was an interview with Reflect’s founder and CEO, Alex MacCaw where we explored the future of note-taking; one powered by AI. In this article, I expound on that vision in more detail, exploring the implications (good and bad), of a personal A.I. that knows your most private thoughts.

Ever since seeing Iron Man in 2008, I've wanted my own J.A.R.V.I.S. A secure, private A.I. companion that can "see" what I see and "hear" what I hear, turning me into a real life Iron Man (minus the billions of dollars of net worth).

So when Humane previewed their AI-powered wearable just a few weeks ago, needless to say, I got excited about where the future was heading. I even wrote about a concept called Ambient Computing that posits that technology will eventually fade into the backgrounds of our lives, only surfacing when needed and allowing us to finally move beyond the screen and engage with the world around us in a more present way.

But there are limitations to this sort of wearable. While it can see and hear the world around you, it doesn’t know what's going on in your own head. For some, that's a comfort. Your mind is the final frontier, unknown by an ever-present A.I. But if the goal was for it to be the most useful it can be, like J.A.R.V.I.S., that's quite a handicap. After all, if you wanted an intimate thought-partner, you'd want it to know you, to know how you thought about the world and how you wanted to process it.

In the end, our A.I. companion is meant to be an extension of ourselves, not another being entirely.

So how do we open up our minds to an A.I.? Well… barring some breakthrough advancements in brain-machine interfaces by Neuralink, most of that knowledge, if it were to ever leave our brains, would probably end up in something like a notes app, right?

How many of us have opened up Apple Notes on our phone and just did a brain dump to organize our thoughts? Or to make a list of tasks? Or journal about a memory we don't want to forget? Writing it down or talking through it with someone is just the natural ways we offload the weight of our thoughts.

These notes are often the most vulnerable things we own so it's important that they're private. But also, it's important that they're accessible by our A.I. self.

That's the future that Alex MacCaw, founder of Reflect is trying to build.

Let's dive in.

The Evolution of Notes

If our notes contain the private thoughts of our lives, it’s important to understand how A.I. is bringing our notes to life. Any good note taking system needs to have three parts:

1. Ingestion - how do you get content into the system?

2. Synthesis - how do you remix and combine ideas within the system?

3. Retrieval - how do you extract the information you need from the system?

Phase I: Analogue Everything

The OG note taking system - the Zettelkasten - would use a system of index cards, meticulously organized with references to other cards in the system, effectively allowing you to follow a path of interconnected ideas across your note taking system. Ingestion, synthesis and retrieval was all incredibly manual and tedious, but it got the job done.

Phase II: Digital Ingestion

Thankfully, by the time I started taking notes, software existed. Around 2010, as Evernote was coming into its own, I got into this idea of a "second brain." A place where I could dump everything in my life and know I could track it down later as needed. Over 4,000 notes later, my Evernote truly was an extension of my own life.

It was incredible at ingestion. It had a web clipper, a mobile app with a page scanner and support for all sorts of file types like images or PDFs.

But synthesis and retrieval were difficult. I had to come up with elaborate systems for organization to ensure I could track down a thought. And trying to pull ideas together from across multiple notes was cumbersome to say the least. I couldn't organize notes within notes without manually copying a note link and pasting it inside of another note. So everything existed on the same plane of thought.

Phase III: Graph-based Synthesis

Then, around 2017, along came powerful outliners like Roam Research and Logseq (and now, Reflect) that made fluid connections between notes through nest-able nodes and backlinks. It allowed me to connect ideas without the need for rigid organizational structures like folders and my brain exploded. My wife could tell you, probably with some degree of annoyance, just how excited I was about this new way of taking notes. I could finally write in the same way I thought! And with Roam's daily notes page, I found myself starting to journal for the first time.

The unfortunate downfall of these outliners was that, while they were incredible for synthesis, retrieval still lacked a certain fluidity because it would require traversing a web of nodes and backlinks to track down all of the thoughts I had on a particular subject.

Phase IV: Conversational Retrieval

Then, late last year, A.I. exploded onto the scene, poised as the perfect companion to these incredible tools for thought.

It could understand meaning and it could converse in natural language. If it had access to our notes, in theory, it could be the perfect ingestion, synthesis and retrieval mechanism. But as with any new technology, there are limitations.

1. Context: For one, the context is too small. You can't pass in every single note you've taken so it's limited in what it can "understand."

2. Privacy: Additionally, with notes being as personal as they are, sending your raw thoughts to a third-party API like OpenAI's GPT-4 feels too... vulnerable. We need a more private option.

3. Size: If you're running a model locally, it has to be small enough (while still being powerful) to be able to run on a device like a smartphone.

What we didn't anticipate last Fall was just how rapidly things would advance in the world of A.I. Several models have now been released with a 100K+ context window, powerful open source models are being released every week that could bypass the need to send our notes to a third-party API, and now there are even decent models that can run on a mobile phone.

^ That paragraph is probably already out of date

By the end of the year, a lightweight, open source model with a large enough context window that it can ingest our entire notes archive could be available offering the privacy, speed and memory that we need to have a truly robust A.I. companion within our notes.

Personal A.I.

Suddenly, I can't help but revisit the idea of a personal A.I. that I envisioned after Humane's announcement. I can see two paths for a “Personal A.I.”, each serving different purposes. Whether or not they’re the same A.I. or two different ones, we don’t know yet.

Humane’s device ingests audio and video and could help you traverse the world around you. But a truly personal A.I. that ingest your notes could help you traverse the world within you.

Imagine being able to analyze your activities to find out what you were doing on the days when you were happiest so you could make time for more of those things. Or find out who are the five people you spend the most time with to make sure you're being influenced by the right friends.

I journal just about everything I do, tagging the people, places, things and activities so that my notes are a living record of my life. I can already answer many of these sorts of questions with the power of queries and a little elbow grease. But there are some questions that still aren't easy to answer.

For example, imagine being able to identify and process unhealthy patterns in your life and have your personal A.I. suggest ways to adjust your thinking or habits to embrace a healthier way of life.

Or, imagine being able to ask it what sort of job would suit you best. It knows what activities give you joy and align with your deepest desires, what skills you have and how you like to work. It would probably even know your salary needs. It can then go and find jobs that meet those criteria that you might not even know exist.

This sort of deep, personal analysis with an A.I. thought partner unlocks a lot of new capabilities including some that have been previously-unknown to us.

For example...

AI-assisted Synthesis

A new superpower has been unlocked... I'm calling it AI-assisted Synthesis.

It's a 3-part cycle that turns the three elements of a good note taking tool - ingestion, synthesis and retrieval - into a flywheel, assisted by A.I.

1. You add some seed material (ingestion).

2. You ask a question of that material and the A.I. responds with a pre-synthesized answer (synthesis and retrieval).

3. You then draw new conclusions and respond, adding new material back into the source context (synthesis and ingestion).

4. As you continue to talk with the A.I., the conversation would eventually yield both breakthroughs (synthesis) and documentation of said breakthroughs (ingestion) for later use (retrieval).

For thousands of years, people have had to spend lifetimes learning about a subject, understanding it, collaborating with others, and tinkering with the knowledge until the right things are in their head at the right time and they can finally make a breakthrough.

In the early days of the internet, the idea of having the world's knowledge catalogued and searchable was almost overwhelming. Rather than manually combing through volumes of books and scientific journals, we could type a simple topic into a search box using natural language and be served up the most relevant information human kind had on the subject. That was an exponential leap in progress for knowledge synthesis and retrieval.

And the fact that you could then contribute to that body of knowledge by publishing your own thoughts and findings onto the internet made the ingestion of knowledge almost trivial. You still had to comb through the endless list of websites manually, but this time, they were filtered and sorted by relevance.

Now, if the insights you had 10 years ago can be surfaced alongside the insights someone else had 100 years ago and the insights you just had 10 minutes ago, you can find connections in near real-time that may have taken a decade before.

Suddenly, the beautiful art of note taking gets enveloped by a bigger, more important paradigm. AI-assisted Synthesis can give each of us the ability to make connections we would have never made before, personal, professional or otherwise. Our note taking tool is no longer just a repository of personal thoughts and feelings. It truly becomes our second brain, storing the cast away ideas, the random snippets of thought we never thought we’d need, ready to pull them together into something new.

How “future” are we talking?

So where are we with regards to this sort of fanciful view of the future?

Honestly... not far off.

Let's take a tool like Reflect. By the end of this year, they'll probably have something resembling an MVP of what I've illustrated above. A local A.I. model that's private and encrypted could run on your device offering you a chatbot that can help you parse through your own thoughts. It would be a place where you can read, write and chat with your notes.

But now imagine 5 years down the road. If Reflect could run a companion app on Humane's wearable device, you could have access to your personal A.I. whenever you needed, and your personal A.I. would have access to the sights and sounds of the world around you, understanding you and your life better with each day.

And since it could be connected to the internet, it could inject knowledge and insights from the world wide web to help you learn and synthesize even more ideas while keeping your personal knowledge repo private and secure.

This hybrid system of real-time ingestion, AI-assisted synthesis and conversational retrieval is a level of super-intelligence that we've never seen before. A personal J.A.R.V.I.S. - a genius thought partner - for every mind.

Call me crazy, but I'm confident that more breakthroughs will happen over the next 10 years as a result of A.I. than we've seen in the last 50.

But aren’t there risks?

I would be naive if I didn't see the potential drawbacks to these sorts of advancements. This could turn into some sort of dystopian Black Mirror episode where we can play back and obsess over negative memories. Or we ask the A.I. about our unhealthy behavior and it responds with an absolutely soul-crushing, deeply perceptive monologue that sends us into a spiral of depression. Who knows what might happen.

Needless to say, there will need to be guardrails. Like with any technology, there will always be bad with the good. I'm a bit of a techno-optimist so I believe the good will far outweigh the bad, but I would understand if you took the opposite position. Some days, I could go either way. We're living on the brink of a new world and we have no idea what it will hold.

But I'm curious to hear your thoughts.

• Is it good or bad if an A.I. can access your private notes?

• Will conversations with A.I.s lead to real breakthroughs like I believe it will?

• What do you think a personal A.I. should be like?

Leave a comment

That’s all for this one - I’ll catch ya next week.

—Jacob ✌️

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You can watch this interview on YouTube, listen to the audio, or read the transcript below.

This interview with Alex MacCaw is Part I of a 2-part series on Reflect. Part II is a deep dive into Reflect and the future of A.I. and note taking.

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Interview Outline

Learnings from Stripe, Twitter and Clearbit

Learnings from CEO Coach, Matt Mochary

Why build a note taking tool?

How is Reflect thinking about A.I.?

Does A.I. eliminate the need for organizational structures like folders?

How will we interface with A.I.?

What’s the impact of having conversations with our notes?

What does the future of Reflect look like?

Disclaimer: The interview has been lightly edited for length and readability.

What did you learn from Stripe, Twitter and Clearbit that you bring to Reflect?

Jacob: You have a history with Twitter, Stripe and then founded Clearbit. What are some of the early lessons you've learned from your experience working for these other companies as well as founding your own?

Alex: Stripe gave me a lot of lessons about what to do and maybe Twitter about not what not to do. I would say I came out of Stripe with a real appreciation for good design. Stripe has a world-class design team, and when I joined Stripe, I was working on some of their designs, implementing them. I was a software engineer. And I wouldn't make them exactly perfect. I would think, you know what, it's 99% of the way there. It's good enough. And then they would ask me to do it again and again.

And that was really good learning, honestly, just getting that level of perfection, it's really important. Especially when you're running your own company. Cause the only person who's gonna say that bar is you. So that helped, that was a great thing to learn.

Jacob: We're gonna circle back around to taste in a little bit, because I really appreciated your article on that, we'll put a pin in that for sure.

Alex: Oh, you have done your homework. Yeah, I'm a big believer in taste companies.

And then, when it came to Twitter, I was there maybe when there were a thousand people and it just really showed me how bloated orgs can become. This was a long time ago and they have only gotten worse since then.

I had no idea what half the people there were doing. I knew they weren't doing much work because I wasn't doing much work. And it's amazing what has happened recently. Whether you agree or disagree with some of the changes being made at Twitter, you can't deny that they are shipping faster and they're shipping faster with 20% of the company.

What does that teach you? That teaches you that shipping speed is entirely a cultural thing? They already had the engineers, they weren't shipping stuff. It is to do with culture and it's to do with internal process and now they're just shipping like crazy.

Jacob: And looking back on their history, they've cycled through four or five different Chief Product Officers in the last five years. And despite each of them being world class in their own right, the shipping cadence didn't really change much during those five years, which is pretty wild.

Alex: I think it is up to the CEO. You have to come in with a sledge hammer and change the culture completely, and that's what happened. One Head of Product unfortunately, is not enough to move the tanker.

What did you learn from world-class CEO coach, Matt Mochary?

Jacob: Speaking of CEOs, you have been, blessed to be coached by Matt Mochary who is a world-class CEO coach. What are some of the takeaways that you've learned from him?

Alex: I got him right at the front-side of his career. Otherwise he wouldn't work with a company my size. But I actually ended up co-writing a book with him, or I say co-write. I lightly edited. He wrote the book called The Great CEO Within, which has got a lot of the things he taught me in that. The great news is you can go and just read that right now to get an idea of what his coaching is.

But I would say it's very process oriented. He comes into your company and he sits down with you and he's in all your meetings and he just sets up these processes so that you can essentially do all of your one-on-ones back to back on one day. And then you have the rest of the week freed up. As CEO, that's amazing because then you have the rest of the week to be creative. But that's one of the main things, man.

There's so much in that. He taught me to really listen, taught me to repeat back, to make people feel heard. He's just the master at systems, like he has a Google Doc for everything. So I really recommend, if people are interested, go and read that book.

Jacob: I believe he also open sourced all of his word docs that you mentioned as well. So anybody can go and check those out.

---

Why did you choose to build a note-taking tool?

Jacob: I wanna move into the meat of this interview, which is Reflect. I've gotta start with why did you decide to choose to build a tool for thought? The way that you've done it has been so beautiful and I'm just such a fan. Bring us into the thinking around why you chose that.

Alex: Let's just say there are no taking apps and then there are note taking apps. A lot of developers do try and scratch their own itch, but typically they leave it at the kind of the markdown phase, which is just the very start. Building a notes app is unbelievably complicated.

Like a proper, full featured one, one that has a WYSIWYG and offline sync and end-to-end encryption. these are really difficult problems. And now are you adding A.I. into the mix as well? This is like the cutting edge of technology to build these notes apps, which is honestly what makes your question even more valid, given that it's so complex.

And yet there are some really good solutions out there. Why did you do this? Why does the world need another notes app? I don't have a good reason for you other than it's a very special passion project for me. I came out of Clearbit, which is a data business that I built to a couple of hundred people, and I hadn't been coding for a few years. I really wanted to get back to basics. I wanted to get back to shipping every day. To writing code. That's what I loved doing. I wanted to try my hand at consumer. I had been in B2B, the whole world. I wanted to see, if I could do consumer, it's a lot harder. I had no idea when I started how much harder it is. But I wanted to try that.

I wanted to build a tool that I use every day that, that, again, that's probably not really possible in B2B. It's at least, it's a lot more difficult. I also wanted to build a small company. Again, B2B is difficult. You have to have a fairly sizable sales and marketing team, and I wanted to avoid all of that. And today, Reflect is just four of us and we serve roughly 2,200 customers, which is amazing that you can do that these days. This kind of company has only been possible in probably the last decade when you have all these tools and frameworks that let you automate things like SysOps, and even when it comes to your accounting, it's all fairly automated at this point. So I can just concentrate on doing it what I do best, which is build product.

Jacob: Is there a particular reason why you chose to go with the outliner note-taking style?

Alex: I was just looking at my dock and thinking, “okay, I wanna build something I use every day. Which of these apps can I replace?” And then I was like, you know what? Apple Notes, I'll build a note taking tool. Yeah. Not a very sophisticated market analysis I grant you, but the whole point of this project was, its a very personal passion and, if there were other people out there who liked what I was doing and had a similar way of thinkings in mind, then great. If not, no worries. Luckily at this point, it's not really a money motivation for me.

And so that was kinda the genesis of it. There are lots of different types of note-taking tools. There are some which are just like strict hierarchy, like maybe Workflowy. There are some that are completely unrestricted, like Apple Notes. You have Roam, which is what I classify as an outliner, where they are just restricted to bullet points.

I wanted something on the fairly unrestricted side. Just because that's how I like writing notes. Everyone's a little bit different. I sometimes like writing with bullet points, sometimes I love writing long essays and I wanted just a beautiful interface that would support both.

How is Reflect thinking about A.I.?

Jacob: Outliners have nodes which are infinitely nest-able. And so you don't get stuck in this hierarchy of folders. And what's interesting with the outliners and back links is we've effectively set ourselves up for success when it comes to A.I. and that's the next part I wanna dive into.

With nodes, having parent nodes and children nodes that, like I said, are infinitely nest-able, there's a a good chunk of context that's built into a single node. And so when you run a query against it, there's a lot that can be done just based on the context of its parent or its children.

Can you talk more about that and how Reflect is thinking about implementing A.I.?

Alex: Absolutely. So if you think about how the brain works and remembers things, it typically works with association. So if you think back to the last thing that you forgot, maybe your keys, what have you, the way you remember is you're like, okay, what do I remember that was associated with that item? And it might be the last room you were in.

With Reflect, you know that person works at a specific company and you may have forgotten their name, but you remember the company they work for. Because all these notes are connected through associations, you can trace this path. And because it's in digital form, it doesn't erode like our brains erode over time, and so we can use this as a backup to our memory.

What's really fascinating is seeing some of these A.I. developments and how they work because, was it like 10 years ago, there was a paper released called, "Word to Vector." I don't know if you're familiar with this, that changed everything. Someone figured out that they could essentially put a word into vector space, and it's like a graph, except, a graph might be 2D. But this is very similar where a word is associated with the other words around it.

And people developed this technology and they started using the meaning of sentences rather than just words. But this is exactly how GPT-3, GPT-4 work. It is incredible technology and all they're doing is looking at this vector space and trying to predict the next word based on the previous words.

So that gets us to Reflect. Like how could A.I. impact this? Like you said, we already have a lot of these associations being tagged through backlinks. We can use A.I. to do this automatically. We can use semantic search to essentially put all of our notes in and the words in them into vector space, and then move through the nodes via vector space rather than having to manually backlink and mark up these nodes.

Privacy Implications

And the potential is massive. The privacy implications are also massive as well. Your notes are your most sensitive items. I think probably more sensitive than anything else, than anything. You can change your credit card number if it gets lost, but you might not even want your spouse to look at your notes.

These are essentially your innermost thoughts. That is why, in Reflect, we end-to-end encrypted the notes. This obviously has some drawbacks. When it comes to A.I., for example, we can't just send all of your notes to OpenAI to vectorize them and ask questions about them. That's the line in the sand that we've drawn.

There are companies out there like Mem for example, that will just send everything over there and I just feel uncomfortable with that.

Running models client-side

So what we are doing is essentially waiting until we can run these models on the client side and it's gonna happen very soon. We have actually been sponsoring a bunch of GitHub projects to make this happen even faster.

Chrome just released WebGPU which is the next thing we need to implement this. So essentially we'll be able to run this models client-side. So what does that let us do?

• It lets us do semantic search so you can search via meaning, not just exact words.

• It lets us do auto backlink population.

• It lets us do predicting the next word.

• It lets us do spelling and grammar detection much better than is natively possible on the OS.

Reflect’s existing A.I. tools

And then we've already done some of the things that we could do already.

So we have a GPT-4 integration and we have a Whisper integration. And GPT-4 is essentially a tool you can use to transform text. So a text prompt goes in one end, different text comes out the other end. What happens in the middle is up to you. You can get very creative about it. We have shipped a GPT-4 integration with a set of system prompts.

And these prompts will let you do things like,

• "Hey, take this big block of text and pull out the to-do items, the tasks, the action items,”

• or, “summarize this text”

• or, “turn this text into a blog post”

• or, one prompt I have loved is taking my favorite writers and saying, “turn this text into a paragraph as if written by this writer.”

And that has been working really well with GPT-4. And we let you save these prompts and reuse them.

Audio Transcription

And then, we have integrated Whisper, which offers nearly human level audio transcription, and this I use every single day. In fact, it has become my primary interface to the computer.

I use a little app called superwhisper.com, which is still under development, but I use that instead of typing now. And inside of Reflect, I use our native Whisper integration to journal. And I've actually found that when I say my journal, rather than write it, it changes in a way that I like. It becomes more filled with emotion. And I cover more things and then I also summarize it as well.

So that's, that is how A.I. can influence Reflect. When I started Reflect more than two years ago, I didn't anticipate A.I. to be at this level already. It's just a happy chance that Reflect came along at exactly the same time as this A.I. is coming along and now it's integrated in the product and it's awesome.

Does A.I. eliminate the need for organizational structures like folders, tags or backlinks?

Jacob: If you have something that exists in vector space, let's say you type a note about how you were feeling yesterday. That semantic meaning is captured by its placement in the vector space. It is relationally and geographically near other notes with similar semantic meanings, right?

Which to me - and I could be ignorant here - eliminates the need for things like back links or folders or any other structural organizational system that notes have typically adopted in the past.

Am I thinking about this wrong or in the future, will we be able to throw notes at a blank screen and it be semantically associated with your other notes and we don't really even have to worry about back-linking.

Alex: Yeah. So and when you say “throw it,” I suspect it'll be transcribed, and you'll be able to hover over any sentence or click it and see a list of all the similar notes, right? It's gonna be amazing. It's very close. I suspect all of this will happen this year.

How will we interface with A.I.?

Jacob: Absolutely. It makes me naturally think about the movie Her. Have you seen that movie? 

Alex: Yeah. I think the future is honestly gonna look a lot like that with a lot less user interface. It'll just be you communicating with this A.I. It'll be very personal. Its prompts will be built up over time, so that they're very accustomed to you. And I think it'll be people's best friend, honestly. It's scary to think about, but it's also very exciting.

Imagine having a genius thought partner to work with, right? Now, if you want a genius thought partner to work with, you have to pay them a lot of money. And most people just can't afford that. You essentially have to start a large business, attract this kind of talent, pay them a huge amount of money, and even then they will do other stuff during their day. They're not ready at your every whim. Whereas this is gonna be like a very personal thought partner that never gets tired and is the smartest person.

Jacob: What's interesting to me is the idea of taking that genius thought partner, combining it with your notes, and effectively you're having a conversation with yourself, with your own thoughts, which to me is mind shattering because I've naturally had to, over the years, go search for something that I may have wrote about at one point.

So I'm navigating through my back-links, trying to find that one thought that I knew I had and I couldn't quite remember. And now with speech-to-text, with Whisper, and then eventually, with text-to-speech all being so fast and local on your device that you could feed it in the context of your notes, you could literally have a conversation with your mind. I'm so excited.

Alex: We're so close, all we need right now is to have slightly bigger context. 

What is the impact of being able to have conversations with our notes… our past-selves?

Jacob: One of the interesting things that Sam, your Head of Growth, wrote about in an article was being able to ask yourself questions.

For example, which city should I live in? It could analyze the thoughts that you've had over the years and say, “you really seem to like all of your vacations to Colorado. You love skiing. You love hiking in the mountains. It's only a five hour drive from your parents and the cost of living is appropriate with your salary.”

Stuff like that could be analyzed and extracted out in a way that maybe, could suggest some cities you've never even thought of before.

Alex: Yeah, that's quite true. Some of those big questions like, where should I live? Who shall I marry? It seems feasible.

You know what I'm looking forward to that’s gonna happen in 10 years time, is the singularity. Essentially, these A.I.’s start improving themselves, and a lot of people don't like talking about this, even though all these A.I. researchers fundamentally believe in it and are working towards it.

And we don't like talking about it cause it sounds absolutely crazy. It sounds like we've joined a cult and that we're waiting for the end times, and I always thought, you know what? Yeah, it does. It's got a lot of religion cult vibes to it. But if I was being pitched to by someone at my doorstep and they were like, “look, this thing is happening in the next 10 years.” And I'm like, sure, fine. And then they were like, okay, let me prove this to you. I'm gonna show you like a little miracle right here. I'm gonna do a miracle in front of you. And then I saw this miracle happen. I be like, “wow, maybe they're right. Maybe they're onto something.” And that's what I think is happening right now.

What does the future of Reflect look like?

Jacob: Every founder has a vision for their company that goes beyond what the company is currently. What is that vision for you, for Reflect? Let's call it the 10 year vision of what Reflect could be in its most - I hesitate to use this word, but - perfect form.

Alex: I'll give you the 1 year vision and the 10 year, cause I think it's quite important not to lose focus because all these shiny things can come along and you can get excited and work on the next thing.

Speed is the priority

Ultimately, if your notes app is not fast or is buggy, then it doesn't matter if you've got the shiniest features integrated. We have been very stable for a long time, but we are not fast enough. Our app doesn't open faster than Apple Notes on mobile. And we also have memory issues with big graphs. So that is like the number one thing we're working on right now, we've got to improve the speed of it. It's gotta open as fast as Apple Notes, otherwise you're not gonna use it instead of Apple Notes.

Task integration in Reflect

I want to build out a really good tasks integration. I think it is gonna be very difficult to do this because I don't want to build a tasks app, and I think you could go down that rabbit hole and I could totally see people still using Things, for example, alongside a Reflect, which is my favorite task app. But to-dos and notes are very closely related. It is our number one feature request. We need to build like a really nice UI for tasks. 

10-year vision

Alright, that said, let's talk about 10 years time. Ten years time, maybe Reflect doesn't have an interface at all. Maybe 10 years time, it's basically just the movie, Her.

I do think ultimately the best kind of UI is no UI. That's why I love using Whisper and audio transcription. I think that could be the future of Reflect and because of the open source movement, it's possible. This is one thing I did not predict. I've been looking forward to AGI for a long time, but I did not predict this open source movement. It's been amazing. And now like over the weekend there were like four open source models released. It's nuts. And it's so cool that we're democratizing this and everyone will be able to run these on their own machine.

So yeah, I'll leave you with that. But thank you very much for, your time today.

I hope you guys have enjoyed this interview as much as I did. If you enjoyed it, please share it so others can learn from world-class builders like Alex.

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—Jacob ✌️

❤️ Smash that heart!

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Last week, everyone was shook by the leaked internal memo from Google that declared “we aren’t positioned to win this arms race” and that both they and OpenAI were being rapidly overtaken by the silent killer that is open source.

Here’s a spicy excerpt from the memo:

“We’ve done a lot of looking over our shoulders at OpenAI. Who will cross the next milestone? What will the next move be? But the uncomfortable truth is, we aren’t positioned to win this arms race and neither is OpenAI. While we’ve been squabbling, a third faction has been quietly eating our lunch.

I’m talking, of course, about open source. Plainly put, they are lapping us. Things we consider “major open problems” are solved and in people’s hands today.'

While our models still hold a slight edge in terms of quality, the gap is closing astonishingly quickly. Open-source models are faster, more customizable, more private, and pound-for-pound more capable. They are doing things with $100 and 13B params that we struggle with at $10M and 540B. And they are doing so in weeks, not months.

—Leaked Google memo, “We Have No Moat”

I wanted to answer the question for myself, who do I think is going to win the race? OpenAI or Open Source? I ran an informal poll on Twitter and it seems most (but not by much) are betting on Open Source.

So I put together a list of the Top 10 competitive moats, wrote out my thoughts on who would win in each category and assigned points to try to determine a winner.

Certainly not scientific but interesting to see the results nonetheless.

Let’s get into it!

1. Brand recognition

If you asked a random, non-technical person off the streets if they've messed around with any of the A.I. tools, in my experience, out of those that say yes most will have used ChatGPT. They've made big enough waves early on and have such a low barrier to entry, that it's a natural starting point for the A.I.-curious.

🤖 +1 OpenAI

2. Intellectual property

Intellectual property such as proprietary algorithms or datasets is all fine and well if you're competing against other closed-sourced competitors. But if you're competing against the masses of open source contributors, it's much slower to build in secret.

We’ve seen glimpses of this by how quickly Alpaca and Vicuna followed the release of LLaMA (2-weeks and 3-weeks respectively), reaching 92% of the quality of ChatGPT, up from 68%.

🌎 +1 OpenSource

3. Network effect

Right now, network effects are slim because each model exists in isolation. You can't really collaborate with anyone yet. However, OpenAI released plugins in Beta which has the potential to have some network effects.

OpenAI can negotiate with the biggest brands to build plugins for ChatGPT because they have the brand recognition and the capital to potentially help subsidize development costs (no idea if they are or not). Assuming open source models can't catch up anytime soon, this could be a point in their favor. Plugins by household brands like Zillow and Expedia means more users. More users means more plugins from top-tier brands.

🤖 +1 OpenAI

4. Economies of scale

Google straight up admitted that open source was eating their lunch when it came to the cost of development. If you're working on Reinforcement Learning from Human Feedback (RLHF) internally, it's going to take a ton of resources.

“They are doing things with $100 and 13B params that we struggle with at $10M and 540B. And they are doing so in weeks, not months.”
—Leaked Google memo, “We Have No Moat”

And if we’ve learned anything from Waze’s community-sourced approach, when you're rallying people from around the world to contribute for free... yeah, it's gonna be cheaper.

🌎 +1 Open Source

5. Cost advantage

This one is tricky. ChatGPT is already free and even ChatGPT Pro is reasonably priced at $20/mo. Open source tools on the other hand are abundant but often unpolished or require a bit of technical know-how to run so the friction is high. I would argue, much higher than shelling out $20/mo.

🤖 +1 OpenAI

6. Superior product or service

Currently, the point would go to OpenAI. However, I'm going to give the point to Open Source because we've seen how fast they're moving. As Sam Altman said, the era of large language models is already over. The new era of creative solutions to difficult problems will naturally fall to open source.

🌎 +1 Open Source

7. Switching costs

There's no loyalty when it comes to A.I. tools. We've seen the tides shift from one tool to another within a day. Tools that effectively do the same thing, but one just happens to get a bit more hype for some reason.

Especially with the introduction of tools like LangChain, different models can be integrated into the same product so there isn’t even a big switching cost for models. So the only switching costs I can think of are ChatGPTs plugin ecosystem. While slim, I think the point goes to OpenAI.

🤖 +1 OpenAI

8. Regulatory barriers

The A.I. landscape is changing too quickly for regulation to keep up. So the odds are that any regulation that comes down the pipe over the next year won't do an adequate job.

More than likely, it's going to be geared toward regulating the larger companies like OpenAI, Google, Meta, Microsoft and others since those are the leaders who are helping inform A.I. policy. That means that the open source community which can move infinitely faster and outside of the geographic and legal bounds of America where the big tech companies are based, will have the upper hand.

🌎 +1 Open Source

9. Distribution network

Open source has an opportunity to win distribution but the chances aren't great. The problem with open source is that it's often fueled by a group of highly passionate users which is nice, but it doesn't get eyeballs. It needs a champion. React is championed by Meta. Android is championed by Google. But open source A.I. models just don't have that going for them (yet). They won't win the wider audience, especially if Microsoft is championing OpenAI's models.

🤖 +1 OpenAI

10. Talent

OpenAI seems to have the leg up on talent. Their team is stacked. But do they have the talent of the masses? You know that jelly bean counting experiment where you could ask a bunch of people how many jelly beans are in a jar and average out the responses and get really close to the actual answer? Well open source is kind of like that. Average out all of the contributions across thousands of talented minds and you're likely to get a better answer than you would from a small group of 1%-ers.

🌎 +1 Open Source

👑 The Winner

So. If you’ve been counting, you know that's 5 points to OpenAI and 5 points to Open Source. Split down the middle. To be honest, I was hoping for a clear winner one way or the other but, as with most things in life, the future isn't clear.

Here's what this tells me…

We're going to see some insane progress over the next 12 months. Competing models, new tools, creative use cases, and fresh regulation (maybe). Much of that innovation will come from the open source community and a lot will continue to be driven by private companies like OpenAI.

At the end of the day, I imagine that open source will soon overtake private companies in leading the charge on A.I. development. But I don't foresee a bunch of non-technical folks adopting open source models to run locally or fine tune themselves. Instead, I have a hunch that OpenAI will shift toward developing ChatGPT into a powerhouse consumer-facing app that will capture the minds and wallets of the general public.

Open Source → will lead Technological Development

OpenAI → will lead Consumer Adoption

I could be wrong about all of this. I’d love to hear your thoughts in the comments.

• Do you have a counter argument for any of the competitive moats?

• Do you think the tables will turn and one will overtake the other?

Leave a comment

That’s all for this one - I’ll catch ya next week.

—Jacob ✌️

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There's a wave of AI companies being created every single day. And I'm going to be honest, I get a little jealous sometimes that they have the luxury of thinking about AI use-cases from scratch because there's so much to be built that has never existed before. In any form. Ever. Blue ocean nerds are drooling at the opportunities right now.

But for the vast majority of people who are leading or working at existing companies, the challenge ahead is very different. Everyone is tripping over themselves to integrate AI into their existing products, some better than others.

Here's my framework for how we're thinking about integrating AI at my company 👇

Let's get into it.

Framework: AI as a Multiplier

One interesting framework on the psychology of money is the idea of money as a multiplier. It posits that if you have bad money habits, the bad money decisions you make when you're poor are multiplied when you're rich. If you have good money habits, the good money decisions you make when you're poor are multiplied when you're rich. It's used to explain why folks who win the lottery or suddenly make millions when they're drafted into the NFL are likely to blow it on unwise purchases and find themselves in an even worse financial position than before they became rich.

I've started to think of AI in a similar way - as a multiplier. If you're a company that has bad bones - no PMF, no differentiation, no value offering - AI isn't going to solve your woes. In fact, it will often multiply them. For a time, you might get some press coverage, you might raise another round of funding, you might get a surge of new users, but the eventually the press will eat you alive, the investors will demand results you can't deliver and your users will churn. All you will have managed to do is waste time and money.

On the flip side, if you have good bones - PMF, a competitive advantage, a high value-offering - those same benefits like a surge of users or another round of funding could be the tailwind you needed to grow incredibly quickly.

Note: I'll caveat this because there's one case in which this framework doesn't hold up: if the company with bad bones needed a technology like AI for their vision to actually work and they were trying to hack together some sub-par version of it until now. In that case, maybe AI unlocks everything else.

AI short-circuits the “path to value”

During the crypto surge a couple of years ago, many companies attempted to bolt on a token or an NFT to their existing products. The problem was that if the application of blockchain technology didn't further the original value prop of the company, it became either an extra source of friction or a disappointment. In the same vein, if a company tries to bolt on AI to their existing offerings, it likely won't be all that useful. For example, low-hanging fruit like text generation or summarization are being added to products that don't need it, simply because it's easy to throw an "AI" tag on your website.

So here's the question I'm going to be asking to help me and my team be more thoughtful about how we implement AI:

How does AI offer a shorter time-to-value?

AI is fantastic at synthesizing a lot of data within a set of guardrails. And it can do so within a natural language chat interface that speaks every language which is possibly the most intuitive interface aside from voice. How can you use that strength to your advantage so that it effectively short circuits the user's traditional path to value?

Let’s play out a simple example. If your product is a library of every historical legal case and lawyers spend hours in your product searching, filtering, and skimming cases for research, adding a summarization or “related cases” feature is a great application because it's expediting the user’s path to the value they already come to you for. But if you're Twitter, using AI to summarize threads is a bit like using a bazooka to hunt a rabbit - it's overkill. The difference in time-to-value is negligible unless the thread bois get more verbose.

Stories from the Trenches 🫡

We're still in the early days of the AI hype cycle, so a lot is going to unfold over the next year. But I think of this newsletter a bit like my own personal diary to clarify and document my thoughts and learnings along the journey as a founder so I wanted to share some of my thoughts from the trenches.

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Before I wrap up, what do you think about these shorter articles that just cover a single tactic or idea that I’m thinking about?

In the past, some of my most popular articles have been the deep dives on companies like Waze, Zillow and Fortnite that extract some lessons from their rise to success. Do you like these shorter ones more or less than those deep dives?

That’s all for this one - I’ll catch ya next week.

—Jacob ✌️

For the past 40 years (since the dawn of the internet), computers have connected us in new and interesting ways. What started as a way for universities to share research has turned into an ever-present tool that has woven its way into the fabric of our lives.

And with each iteration - from mainframes, to desktops, to laptops, to mobile phones - computing has moved closer to us. From research facilities to our home. From our home to our lap. From our lap to our hands. And why wouldn't it? That lightweight, thin device that easily slips into your pocket is the key that unlocks a whole new world. Today, leaving your house without your phone feels almost unreasonable.

But as many have pointed out before, our new superpowers come at a cost. To interact with our devices, we must stare into its glowing screen. But it's where we work. It's where we play. It's where communicate. It's where we create. How are we supposed to give up something that we rely on so much? At the risk of sounding overly dramatic, in our empowerment, we've become imprisoned.

A Future of Ambient Computing

As more attention has been drawn to the negative impacts of technology, especially on the younger generations who grow up with it in their hands, there has been a growing movement of people advocating for less screen time.

Nature retreats with no technology and phone-free dinner parties have become a mark of health and balance. There's even a shift back toward "dumb phones" like the Wisephone and The Light Phone that attempt to offer only the most basic utilities.

But these solutions are incomplete. While they give us a path to re-engage with the world, distraction-free, they demand that we relinquish our superpowers. In service of more connection, we cut off the very device we use to connect most often. In service of "decluttering" our minds, we cut off the tool that helps us synthesize and organize much of our life.

A complete solution must find a different path to re-engaging with the world around us. One that allows us to augment our capabilities with the technological superpowers that we've become accustomed to without it getting in the way of being present. A path where technology fades into the background until it's needed, rather than demanding our attention at its whim.

In his final column for The Verge in 2017, Walt Mossberg coined the term "ambient computing" to describe this new path.

"Your whole home, office and car will be packed with these waiting computers and sensors. But they won’t be in your way, or perhaps even distinguishable as tech devices. This is ambient computing, the transformation of the environment all around us with intelligence and capabilities that don’t seem to be there at all."

—Walt Mossberg, The Disappearing Computer

Just as smartphones revolutionized the way we interact with information and each other, ambient computing will, once again, transform our relationship with technology, making it more natural, unobtrusive, and contextually aware.

Ambient Computing is Humane

Until recently, that vision of the future has only revealed itself in glimpses through devices like the Amazon Alexa or Apple Watch. Computers that are there, but out of the way until needed. But Alexa is only available in spaces where an Echo device sits. And an Apple Watch is limited in its capabilities. The full realization of this future depends on a paradigm-shift in technology. Something... like AI.

• AI could detect your intent without a glowing screen using your voice or gestures.

• AI could spawn agents who will perform a task autonomously with little to no input.

• AI could fade into the background until called upon.

• AI could serve up a distraction-free interface: speech.

Is AI the breakthrough we need to balance our technological superpowers with being present and engaged with the world around us?

A company called Humane, founded by two ex-Apple employees who worked on era-defining products like the iPhone and iPad, has raised over $200 million to build the future of computing. Their idea is to use sensors to take in your surroundings, your voice, your wants, needs and intents and then become a personal assistant similar to Iron Man's Jarvis, answering questions, providing information about the world, and generally being helpful.

"Imagine going on a run, and the music you're listening to is dynamically scored based on where you're running, what time you're running and how fast you're running.

Or imagine it's the morning of your big presentation and you're advised that a meditation would help your anxiousness and rapid breathing. One by the window, listening to the rain would be best. Soon technology will intelligently handle all of this for you, instantly, allowing you to remain in the moment.

Imagine being able to walk down a road and completely understand what's inside every shop before you walk in or without interfaces getting in the way so you can fully explore things.

Or imagine being at your friend's wedding and not having to waste any precious time capturing that perfect photo, staring at your screen and not into your friend's eyes. Instead, AI will free you to experience these moments as they happen and capture the memories on your behalf. It will allow you to live in the moment now and not just relive that moment later.

Imagine being able to literally shop the world. You're walking down the street and you see something that piques your curiosity. A person wearing a particular brand of sneakers or a new beautiful dress. Now imagine being able to get instant answers to your questions. Who designed it, where do I buy it, is it available in my size? All of your thoughts answered in milliseconds. And it's screenless.

A world without a display where your surroundings becomes your operating system means less demanding and distracting screens. No more complicated, time-consuming interactions. A screenless future allows us to focus on what really matters - being human."

—Imran Chaudhri, Co-founder and President of Humane (source)

When they demoed their product last week at a TED event in Vancouver, they showed off a handful of delightful features such as voice translation in the speakers voice (English to French), a "catch me up" summarization feature, a simple phone call and even a food analysis feature that could tell whether or not you should eat something based on your particular food sensitivities.

Dismissing the idea of augmented reality glasses or contacts as being too distracting, their goal is to help us become present, engaging more with the world around us and less with a screen in front of us. This could be the realization of that idea that Walt Mossberg described in his 2017 article, "The Disappearing Computer."

Ambient computing.

What does this mean for us?

"We believe that the best user interface is no user interface. And ambient computing will finally enable us to stop looking down at our phones and start looking up at the world. This, to us, is the future and it's closer than you might think."

—Imran Chaudhri, Co-founder and President of Humane (source)

As someone who enjoys thinking about and designing interfaces to be accessible, useful, and beautiful, I'm sad to think about screens being replaced but also excited to explore and perhaps even help invent the interfaces of the future (a topic for a future article). Will it be voice? Gestures? Holograms? Augmented reality? Or some combination of them?

As we watch a new era in computing emerge, we have a choice. To be the ones who are slow to adapt or to be the ones who embrace the potential of this emerging era of ambient computing. To embrace a new way we experience and interact with technology where our digital superpowers are seamlessly integrated into our lives, allowing us to truly live in the moment.

What do you think? Is ambient computing a positive future where we can be present more than ever in a world of technology or one that’s asking for government overreach and the prying eyes of big tech? Let me know in the comments.

That’s all for this one - I’ll catch ya next week.

—Jacob ✌️

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My "Interview × Deep Dive" combo is my favorite series that I write. It does two things:

1. Interview — It gives us the opportunity to learn from world-class founders and product leaders (find my other interviews here).

2. Deep Dive — It gives us a space to imagine the weird, creative, mind-blowing ways the world might change and, hopefully, inspires us to build something great.

Last week I released my interview with Nan Yu, Head of Product at Linear and this article is the Deep Dive sibling that attempts to answer the question, "what would the world look like if Linear achieved their vision?" It's an exercise in understanding how companies think about the future and, importantly, what we can learn from it.

I try my best not to get TOO far fetched, because I want to stay true to the ideas and the vision cast by the company I'm writing about. But I also give myself room to expound on their ideas because while they probably won't end up building what I write about, it might inspire someone else to build it.

I lead off with this context because this particular deep dive will not only be an exploration of what Linear might become but also a set of predictions about how product management will evolve.

Intro to Linear

If you're not familiar with Linear, it's a project management tool that's taking on multi-billion-dollar behemoth's like Jira and Asana. And at first glance, there isn't a big "key differentiator" that smacks you in the face. It includes the Kanban boards, timelines and tasks that you'd expect from a project management tool along with a handful of features that give it a modern flair like a Cmd+K menu. But overall... pretty straight forward.

Until you start using it...

Linear has built an incredibly loyal following because of their unique method. It's precise. It's simple. And though this word is painfully overused, it's opinionated. And it's this method that not only drives the product forward but, I believe, will change the face of product management. Dramatic much? Maybe... keep reading and you be the judge.

Let's get into it.

Linear's Vision

If we look at the landscape of project management tools, they're almost entirely built for PMs. They boast infinite ways to customize, filter, sort, and otherwise manipulate your data. And while it was originally built with good intentions, I believe it's slowly turned into a counter-productive form of job security on par with overly complicated legalese.

A PM might be world-class at product strategy, or product marketing, or data analysis but the endless knobs and dials in current-day project management tools often lead to them spending more time moving data around than actually doing the higher-level work.

So what if that weren't the case? What if managing tasks were simple and running reports were automatic? What if you could get the answer you need in seconds instead of hours, empowering you to make informed decisions and move the product forward?

That's what Linear is trying to do.

In our interview last week Nan, articulated a telling vision for Linear:

"I don't want you spending your entire day in Linear. That would be a failure for us if somebody just spent their entire life managing issues here and there."

—Nan Yu, interview with The Glimpse

For product nerds, reducing session time is a sort of holy grail. The journey is dangerous and there are a thousand ways you could die, but if you reach it, you are granted eternal life.

Down one path, the session time slowly drops, deceiving you into believing you're doing the right thing. Except it keeps dropping and dropping until your users eventually churn and you die a painful death. But down the true path, you'll evade traps, climb mountains and cross roaring rivers to build a product that is SO GOOD that your users get the value in less time this month than they did the month before. The holy grail.

It's a bit meta, but Linear is building a product to help others build products by getting them out of Linear and back to the product they should be building... does that make sense?

Let's move on.

The Evolution of Product Management

Now that we know what Linear's vision is, we can break down the unique method Linear is taking on their quest to find the holy grail. I'm going to structure it in a series of four predictions that act as check points along the path to the grail. As each prediction becomes reality, Linear will take another step toward their vision.

Prediction #1 — We'll return to the basics

"If you're working on a project, it's called a project. It works how you think a project ought to work. Okay. Now that you've taken the five seconds it takes to learn this, go and actually produce software like you intended to do."

—Nan Yu, interview with The Glimpse

Over the last 50 years, different project management methodologies have evolved such as Agile (Kanban/Scrum), Waterfall, Now-Next-Later etc. And while Linear has the bones for each of these methodologies such as a Kanban board, roadmap and timeline, Linear has intentionally shied away from branded words like "user stories," "scrum," or "sprints" in an effort to clarify the work to be done.

Prediction #2 — We'll see highly leveraged PMs assisting larger teams

Linear avoided having a dedicated PM for years. It wasn't until recently that they brought on Nan to lead product and, admittedly, his role isn't your traditional PM role. He stays high level, connecting teams and ideas across the org to ensure product cohesion. His ability to not get lost in the weeds of task management while running product across 25 engineers and 2 designers is a testament to Linear's ability to return to the basics.

"In terms of working on the web app and the Electron app, we basically have two designers and I wanna say 20-25 engineers. And I'm the only dedicated product manager."

—Nan Yu, interview with The Glimpse

As tools like Linear continue to get better at offloading the drudgery of product management, and the capabilities of AI expand, I believe we'll see more and more companies adopt Linear's unusual PM-to-Engineer ratio.

We're already seeing glimpses of PMs learning to embrace the raw power of AI. Lenny Rachitsky recently wrote a great article on the creative ways PMs are using ChatGPT to execute their PM duties faster and better than ever.

ChatGPT isn't a PM-specific tool, nor does it have access to product code or issue databases and yet it's still proving to be a helpful tool in the hands of curious PMs. Imagine what an AI-powered Linear could do!

Prediction #3 — We'll let AI handle rote PM tasks

The last point may have made it seem like PMs will soon be out of a job but I don't believe that'll be the case. Instead, I think we'll see AI augmenting a PM's responsibilities, empowering them to do more than ever before.

If you read the article from Lenny's Newsletter I mentioned above, you'll notice a theme emerge. AI isn't as good at idea generation as humans, but it's fantastic at idea analysis. Give it some raw materials, guardrails and a directive and it can execute very well.

• Give it a PRD and it can break it up into the appropriate issues and plot it out on a timeline.

• Give it a spreadsheet of user feedback and it can identify the common ideas and rank them by urgency and difficulty.

• Give it a data structure and it can write complex queries against it.

• Give it a data set to analyze and it can extract patterns that we might never see.

• Give it a thesis and it'll make the opposing case to help you reason through your position.

• Give it a rough draft and it'll polish it in the style of the company's persona.

What does this mean for PMs and founders? I think it means we'll begin to see a separation between the rote tasks and the creative or strategic tasks. As AI begins to handle more of the "grunt work," it will free up PMs to live at a higher level like Nan, connecting teams, ideas and objectives across the company or product.

Prediction #4 — We'll interact with data conversationally

One of the most important responsibilities of a PM is data analysis to help inform the decisions of the team. Admittedly, this is one area I'm not super great at. I have the right ideas of what I want to measure or understand... but actually getting that data and then slicing and dicing it so that I know what I'm looking at is accurate is more illusive. This is one of the areas I'm most excited about seeing AI integrate with.

"A thing that AIs are good at right now is translating natural language into sort of structured language, right? I have a new idea. Maybe there's a fifth user persona that we weren't considering. Now, can we retag every issue according to this new taxonomy? And previously that would just be like the most annoying thing ever to do in the universe, right? And now it's like, “just let a computer take a first pass at it,” right? And then we can just see if there's something that makes sense."

—Nan Yu, interview with The Glimpse

To Nan's point, AI is really good at understanding intent. So, let's say you have a bunch of data that would take you a week to manually tag before you can analyze it and extract some meaningful insights. AI can ingest and analyze it in minutes, turning a week of work into a simple question answered in real-time. Plus, you didn't need to add some single-use custom tag that clutters up your database.

Conclusion

As one of my Twitter followers so aptly argued, Linear's $5M in revenue is a drop in the bucket compared to Jira's $800M. Isn't it a bit grandiose to believe that they could topple such giants? And I agree - it feels *almost* far fetched. But if you'll humor me, I think I can make a compelling case.

To get a better picture of where Linear stands currently, it's worth looking at their client list which boasts the likes of Cash App, Vercel, Ramp, Retool, Loom, Descript, Arc, Mercury, Levels, Raycast, Superhuman, Unsplash, and OpenPhone. These names are among the hottest young tech companies.

Jira has been around for 20+ years. Give Linear that sort of timeline and I have no doubt that we'll see them not only become irreversibly integrated into the rising stars of the next two decades, but become embedded in the startup zeitgeist. If you're starting a company in 2023, which do you choose - a 20-year old, bloated app or a sleek, fast app that gets out of your way and lets you focus on what you do best: shipping product.

What are your thoughts?

That’s all for this one - I’ll catch ya next week.

—Jacob ✌️

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You can watch this interview on YouTube, listen to the audio, or read the transcript below.

This interview with Nan Yu is Part I of a 2-part series on Linear. Part II is a deep dive into Linear and the future of product management.

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Interview Outline

1. What is Linear’s vision?

   1. How Linear hopes to change product development

2. What is the Linear Method?

3. How is Linear opinionated?

   1. Why Linear says “no” to custom and required fields

4. How does Linear build product?

   1. Building Linear Insights

   2. The role of product in an engineering-heavy org

5. How does Linear set goals?

6. How does Linear gather user feedback?

   1. How do you avoid bias in user interviews?

   2. How does Linear organize user feedback?

7. How does Linear think about the role of AI?

Disclaimer: The interview has been lightly edited for length and readability.

As an aside, Substack just launched Notes - you can find me there :) And if you find something you like in this article, highlight it and you can “restack” it!

Join me on Notes

What is Linear’s vision?

Jacob: Linear is taking on some giants in the space like Jira and Asana. These companies are worth tens of billions of dollars and are used by virtually every product company on the planet.

It feels almost like a solved problem and yet the popularity of Linear has proved that it isn't. What is the vision behind Linear that drives it to create a product in a red ocean and be able to do it so well?

Nan: There's a lot of things to consider. I say this with completely honesty, right? We have a lot of respect for what Atlassian has done over the course of that company's lifetime. They effectively built out this particular space, right?

Jira is like a de facto standard for a lot of people's experience. And no one ever got fired for installing Jira, right? With any kind of incumbent like that, like they're always going to look like they have a sort of market dominant position.

And I think it's been tempting for a lot of startups to try to compete with Jira, right? If you ask engineers on the ground, the app it's starting to show its age. I don't know exactly what their NPS is, but if you look at an incumbent with a big market cap with relatively low NPS amongst day-to-day users, it's appealing for a certain kind of business to want to try to figure a way in or try to figure out, is there something new we can do in this space?

When we think about what Linear's perspective is, I think you of have to look at the founders of the company and the sort of people who are early on in terms of developing the app. Like we all came from a background where we got into software because it was fun, right?

We got interested in software before it was like, “oh, we make really great six figure salaries and you'll work at Facebook and Google and have a great career.” Like we were into this because we were hacking like my MySpace pages and making flash games and screwing around with computers in all sorts of different ways, right?

And we're from that kind of generation and it was fun. And we got into the corporate setting and all of a sudden became like really un-fun in all sorts of weird ways. So I think like overall, we think that we could have an opportunity to introduce ways of working and ways of developing software which make it fun again. And I think that there's a real sort of emotional opportunity there for people to adopt Linear.

How Linear hopes to change product development

Jacob: If you fast forward, let's say 10 years, maybe even five years into the future, how do you see Linear changing the landscape of product development?

Nan: Yeah. I don't want you spending your entire day in Linear, right? Like that would be like a kind of failure mode for us if somebody just spent their entire life managing issues here and there.

So I think the utopian vision is that a lot of people who use Linear don't even know they're doing it. There's a big piece of what we're trying to do to make it pretty transparent in terms of just like what needs to be done and what kind of work is being done at the company and how software is being developed that lets people focus on their actual jobs and the stuff they're experts in, the stuff that they're interested in without focusing on all of the meta work. So a lot of the decisions that we're trying to make are trying to minimize the footprint of Linear in people's actual day-to-day lives.

What is the Linear Method?

Jacob: That's such a beautiful metaphor, I think, for good design as well. Good design disappears, so does good products. Products that actually do the work for you and fade into the background and allow you to focus on other things is a really cool vision for it.

I want to transition into the Linear Method because it actually dovetails beautifully. The Linear Method seems to cut through the fluff of the industry. We've built up this product manager persona in a way that says you have to have this sort of process, this sort of framework, you have to organize the tasks really well and have this great Kanban board or prioritization system or whatever.

And in reality, what we're trying to do is get from point A to point B from ideation to production ready code in a short amount of time as possible and do it efficiently. And so one of my favorite parts of the Linear Method is write issues clearly in plain language instead of using user stories.

It harkens back to this day when it was important to translate non-technical user feedback into technical documents or something. But these days everybody knows what a notification is. Everybody knows what a login screen is. And so you can just say build a login screen and that's gonna be enough to provide direction and boundaries for your engineering team.

What is the impact of the Linear Method on Linear's growth and adoption?

Nan: Yeah. And you raised a good point about, like the timing matters, right?

There was a moment in time in the sort of practice and history of software development where like user stories might have made a lot of sense because people were really wrapping their heads around this idea of translating business requirements into actual working software.

And, one of the things you pointed out is you're like, look, a lot of these patterns exist already. You can just say, do the pattern, instead of trying to indirectly tell people that you want the pattern, right? The user ought to be able to like, input their email and password in order for them to authenticate.

It's okay, you want a login screen? Thank you. And the Linear method is related to that, right? There's a lot of words that get thrown around in the industry a lot like agile and Sprint and epic and ninjas and all that kind of stuff.

And, we created those words partly to brand some of these ideas. But like the more you brand it, the more they take on a life of their own. And I think we're at a point now where everyone knows how these things work. And that gives us an opportunity to simplify the language and simplify the communication back down to what are you actually trying to get done.

Because honestly we think back to the original point that like software development and writing code and designing and things like that, like those are really interesting, deep, fun activities already. You don't need to make it more fun by calling people ninjas. That's not a thing that you need to do. And I think the Linear method is really about just like setting up a framework for people to retain their focus and put it back onto the actual task at hand.

Jacob: There's been other companies, I believe Basecamp is one, that's written a manifesto that says, this is the way that we view the world and why we're building software this way.

And it I think the goal of the manifesto has always been to attract the sort of people that see the world through the same lens that you do. Has Linear had a really visceral response from the community through the Linear method? Or is the Linear method more of an internal playbook?

Nan: I think as much as anything, it's a sort of framework for people to understand how the software is designed to work, right?

A lot of times onboarding is really hard because the question that people ask us, like, how is this supposed to work? I wanna do things like the right way. That's like something that we hear a lot when people kinda adopting Linear for the first time.

And what we are trying to emphasize is in your mind, simplify it down to its most fundamental components and that's the right way. And I think it's much easier for us to communicate that than to come in with, specific terms that we made up.

The Linear method is a way of expressing that, right? If you're working on a project, it's called a project. That's what it is. And that's it, right? There's no other weird word that we came up with for this.

It works how you think a project ought to work. Okay. Now that you've taken the five seconds it takes to learn this, go and actually produce software like you intended to do.

How is Linear opinionated?

Jacob: I think that the Linear Method has caused a lot of people to call Linear, opinionated. What are some of the strong opinions that Linear has built into the product itself that maybe people say, “wow, it might not be flexible, but this is exactly how it should work.”

Nan: I think this is gonna cause some of our users to feel a certain way because there're two feature requests we get all the time. And one of them is like custom fields and related to that, required fields, right? Those two kind of come hand in hand. And another one is like multiple assignee to an issue. And, so far, we've said no to those requests consistently over time. Hundreds of times.

And I think that is a strong opinion because like at the end of the day, we value clarity over almost everything else. We're like, for the day-to-day engineer doing their job when they have to either create a ticket or close an issue or do some work like they really need to know what is going on, right? And if they don't know anything at all but they just know who's responsible for this thing, they at least have someone to go to and like figure out “okay, that's my point of contact, I need to talk to 'em about this.” Or, “that's the person responsible, I can rely on them to get it done.”

Or if that person's me, it's “okay, everyone's relying on me to get it done.” Those are extremely valuable to hold at scale. And it's worthwhile to sacrifice a lot of flexibility in order to just maintain that promise to every single engineer on the ground.

There are disadvantages, right? Sometimes it's these two people working on it together. It's like, look I get it. We're thinking through a lot of different solutions to kind of maintain the clarity about it, but also let you express those kinds of ideas in the system. But, as a starting point, we're always gonna prefer clarity over every other trade off.

Why Linear says “no” to custom and required fields

Jacob: And people might create a million different fields, muddy up the water of the issue. But if there's something that's recurring and everyone would love to create this particular custom field, then maybe that's an indication for Linear to build a feature set around that problem that they're having and make it a first class citizen of the product as opposed to it being this hacky custom field solution for it. Am I on target with that?

Nan: Yeah. I think that's one of the approaches that we've taken to field these kinds of requests, right? There are things that are first class citizens of Linear that we think are important just broadly speaking, for most issues. And those things we're happy to add to the feature set.

The problem with custom fields is sort of twofold, right? Like the first one is like what you said, like people had a million of them and then now you have no idea which ones are actually needed for a particular issue versus another one. Do they have the same custom fields? There's all sorts of configuration and sort of tribal knowledge that everybody needs to know.

And then the second problem is people then start using the hammer of oh, we'll make some of them required. And then you invariably have the this required field, but it's actually not applicable for the thing at hand, so I'm gonna put in some kinda placeholder, meaningless value to it.

And then your data quality just goes down. So our perspective has always been like, when we see other tools building these kinds of things, usually what's motivating it is there's some kind of like reporting need, right? From the management layer or above.

And they're like, "Hey, like I want to be able to understand my issues from this perspective." And in order to do that, I'm gonna force down the tedious work of doing the data entry on everybody, right? On everyone who's like doing the day-to-day stuff. And we've taken the inverse approach, right?

Which is that let's really make sure that we're always focusing on making everyone's day-to-day experience really excellent. And that's the way you're gonna get good data quality, right? We get this feedback from customers all the time which is, “for the first time I can actually rely on the issues to be like correct and up to date.”

And it's because people understand why doing whatever data entry is actually necessary is going to add value to their life. And then they're motivated to do it. And then from that foundation you can actually have reporting that's useful.

You can have very nicely structured reporting that is bad data quality, and it's just garbage and garbage out. And we're just like, “let's avoid that situation,” right? Let's make sure the data quality's always good by making the day-to-day experience really excellent. And then we can solve the reporting problems independently.

How does Linear build product?

Building Linear Insights

Jacob: I love that you mentioned that because y'all just released Insights, which is this gorgeous reporting feature built right into Linear. Let's talk about the process from idea all the way through to production and kind of walk me through how product gets built at Linear.

How did that idea get on the table? What kind of conversations were had around it? How did you get it to design? How'd you get it to engineering, et cetera, et cetera.

Nan: It's a funny story, because this is the first thing I worked on with Linear. I actually started off just as a contract product manager with Linear during the start of this process where they're like, we keep hearing it from customers. Everyone's like, "we need reporting. We need reporting." The IC's are loving all of the day-to-day work, but the managers are feeling like they're flying blind here, and we just need to be able to understand the numbers behind a lot of these things.

And everyone on the team says, okay, so clearly there's a need for some kind of reporting, but we don't wanna be in the business of building like a complete BI tool on top of our existing system, right? That's taking on so much scope.

We also don't wanna be building really underpowered tools that tick a box. Okay, you can buy Linear now, it's got reporting because it says so on the feature matrix. But then people use it and it has an outrageously steep learning curve or they just don't find it useful, right? We wanted to avoid those things.

And that's why I was originally brought in. We wanted to do a lot of research about what kind of reporting do people actually want to be doing out there. We took feedback from different customers who are asking for it, different sales conversations that we had where people expressed this is a requirement.

We read a lot papers and books written about reporting on Agile development processes and things like that. And okay, what can we take away from this that we think is like valuable, but what are the core concepts?

So there was a pretty extensive research process and but even by the time I had landed on this particular project the team had already done a lot of like user interview and stuff. So they had videos and notes and things like that to review.

Before we started developing on it, we felt like we understood the sort of key problems that we were trying to solve in the space. And the sort of design process for it was really about like simplifying that down to the minimum set of things that we can do to actually solve these things.

And we're not trying to be overly minimal. We don't wanna be austere about it. If you look at insights, there's a lot of features in it. But we wanted to center on key principles that make people really want to use reporting features.

#1 — Attention

And where we landed was, a lot of it was like, people wanna know where they're spending their effort. They wanna know where the team is spending their attention. That's like one major locus of where those requirements ended up coalescing around. Another one was they wanted to know how long things generally took.

#2 — Time

And then the sort of variant on that, it's “hey, if everyone does like estimation and we all know it's wrong, but how wrong is it actually?” Always wrong. But is it useful wrong? Or is it like random? People wanna know that. Presumably every single task management system can tell you how long things actually take, but like very few of them actually do.

#3 — Data

And then the third piece was really about data quality more than anything else. “Hey, we have this policy where, when a bug comes in you should try to label it with what customer reported“ or something like that.

We have this sort of soft policy and sometimes it makes sense, sometimes it doesn't. And you know what, it only makes sense for a subset of things or whatever it is. It's a very complicated rule set. I just wanna know of like, how good are we doing in that particular idea, right?

Or we're trying like to run a particular process. How well are we actually achieving the intent. And then you want some kind of reporting to just give you some aggregates about that kinda stuff. So those are the main areas, right?

So we're like, okay, let's make reporting that can answer those questions in a way that's like flexible enough because every company is incredibly different but it also makes a lot of sense within the Linear ecosystem, right?

We wanna make features that make sense in the context of everything else that kind of existed. So yeah, those were the main considerations that we had during the sort of initial research and design process. And we had a working version internally very early on, right? For us, it's super important to dog food the absolute heck out of every single major feature that we develop. Within maybe like a month of development, we had an internal working version that was not nearly ready to go for primetime, but like we could use it to do work and figure out what was good and bad about it.

1. And then we iterated,

2. we went through a private beta process where we teased it and had signups,

3. we went through a public beta process,

…and we're near the end of that public beta stage and we're maybe a couple weeks out from being ready to go to call it done.

The role of product in an engineering-heavy org

Jacob: Linear is known for having engineers and designers working on the problem at the same time. What did that look like in terms of bringing some insights from a customer interview to the table and then actually trying to solve that problem?

Was it, “hey, maybe this is a direction based on this user interview, can you go design something real quick and let's see how that looks? And engineer, is that buildable based on our current stack?” What does that conversation look like to involve design and engineering at the same time?

Nan: Yeah. I think in order to understand that you have to understand our staffing profile at the moment. In terms of working on the web app and the electronic app, we basically have two designers, right? And I wanna say 20-25 engineers or something like that.

So that's not a ratio you'd like normally deal with. And I'm the only dedicated product manager, where that's like my entire role. Almost as a forcing function, I have to stay very high level, and the designers have to stay pretty high level, right?

They're not gonna get into the pixels of every single feature that we build or anything like that. And then the product engineers are the ones that are doing a lot of the design work, design decisions as they're iterating on things. So I think that's something that's a little bit unusual about how Linear works.

Like it's it's default collaborative because we literally don't have the bandwidth to do the sort of like full separation of responsibilities like you would normally see.

Jacob: I know that your role is a very unique one in the world of product, but I want to dive into it because it fascinates me. Linear is very vocal about building for engineers versus building for product managers.

Presumably because engineers are the ones actually executing on the work, and they need to be able to take tasks and translate that into, "what am I supposed to do today?" How does your role as a product role insert itself into an organization that is very engineer driven?

Nan: I think the way to think about it is my primary responsibility is to connect different teams to each other. That's fundamentally what I'm doing. I'm taking a development team over here and then the one over there working on different issues working on different projects and features and saying, okay, like how do these two lines of development make sense?

Like at the same time or with each other? How do these features interact? Linear's a very horizontal type of product. There's a lot of different features for different parts of the development process. And they all have to make sense together. No one likes going into a piece of software and they can see the org chart from this piece of software because this thing and this thing are just totally removed from each other. They look like they were designed by different teams.

A big part of what I do is connection and integration both across dev teams like I described, and also up and down, right? What's the business strategy? What does that mean in terms of the customer segments we're targeting and how does that make sense with what's being developed and what the roadmap looks like.

If I'm able to connect the development to the business and teams to each other then I'm doing a good job.

How does Linear set goals?

Jacob: What is the process for setting goals at Linear?

Nan: Yeah, so I, I think a lot of a lot of companies get into trouble if they try to be too goal driven. Like, we set OKRs and that trellis down all the way to like individual contributors and then people are trying to set their own OKRs and just like shrug and pick a random number cause that's the best they can do.

So we're trying to like, avoid that kinda thing. For us, goals are at the level of managers. The goals themselves are pretty high level, right? There's like certain customer segments that we're trying to target. There's certain objections that we're trying to get past for things like enterprise accounts and things like that.

And so even just with that basic starting point like we leave it up to the creativity of the teams to figure out ways to attack it, right? Like we have to:

1. Take apart the problem,

2. go to the data a little bit,

3. understand what kind of user behaviors are driving a particular metric or number that we're seeing,

4. and then form some kind of hypothesis around how we might be able to solve that.

5. And then after that it's mostly about execution.

So I think like with everything else that we do at Linear, it's a fairly like minimal and simple process.

We either got the customers or we didn't. We either had good adoption amongst a particular market segment or we don't. These are not things that we need to overcomplicate and we try to get these as directly as we can.

How does Linear gather user feedback?

Jacob: Obviously, you guys have a very active community. A lot of people are vocal about Linear.

Are you getting on the phone and calling people for user interviews regularly to try to prove or disprove a hypothesis? Or are you just combing through user feedback that's already coming through other channels? What's the process for making sure the data that you're basing decisions off of is good data.

Nan: So I have pretty strong opinions about this. I think that you're basically trying to triangulate three things, right?

1. Passive User Research (User Feedback)

Like the first one is a backlog of customer feedback. You have to have a pretty decent way to record that in a way that's structured enough for you to go reference it. I think that there's a lot of interesting things that we're trying out internally that's probably gonna make its way into the product at some point, but that's one step that you have to take and make sure you have that really buttoned up.

2. Product Strategy

The second thing is like understanding what the strategy of the product in the company is. There's a core strategy that we're taking, right? We're really focusing on the developer persona, making their life really good, adding layers on top of that. That's the strategy. That's not set in stone. We can change that if we want. But we’re saying, okay let's get an understanding of what the customers are saying to us and try to match that with what the strategy of the company is.

3. Active User Research (User Interviews)

And then the third piece is what you're talking about, which is like going out and doing active research around stuff. And I think active research is a little bit dangerous, right? People are like, I have a hypothesis and they go out and they ask questions that would like, lead to “oh yeah, here's why you're correct,” right? This is a very human thing to do.

You can shake your finger at people and say you should try to be unbiased in your interviews and stuff like that. You can try as much as you want, but it's pretty hard to avoid in some circumstances, especially if you're just really trying to get like an understanding of the details of whatever hypothesis or theory that you're putting out there.

But I think a healthy balance is trying to understand that is gonna be biased to some degree and then matching that to the company strategy.

How do you avoid bias in user interviews?

Jacob: So going back to the question about user interviews, how do you do that in a way that is trying to remove as much bias as possible, but also dig into the deep questions around that user interaction in a way that's informative for the team?

Nan: Without getting too much into like interview theory and things like that, I think one of the things that you wanna do is honestly just acknowledge that it's gonna be biased to some degree. That's the first thing I start with. I'm not trying to minimize the biases. I'm trying to control it. I'm trying to control the risk of bias.

For example, we're working on roadmaps right now, right? We're trying to make a roadmap feature a lot better. And one of the things that we ask is, "Can you show us how you communicate? How projects in the engineering department are coming along? Show us like what you are actually doing to communicate those things. What emails are you sending, what reports are you building, what spreadsheets are you maintaining?” All that kind of stuff. And that's good.

It's not gonna tell us whether having these kind of high level views are important versus not, right? We have to have some kind of independent theory to describe that, but we're not asking questions about, “how do you wanna improve the roadmap feature inside of Linear?”

Like we're trying to figure out what people are doing of their own accord and things like that. So just taking that more oblique approach to understanding the topic is probably the go-to tactic that we're trying to do to make sure that we're not just like seeing everything through rose colored glasses and giving ourselves a lot of confirmation bias.

How does Linear organize user feedback?

Jacob: You've mentioned that the suggestions and feedback that you get from customers through Slack channels or Twitter or any other feedback mechanisms that y'all have in place gets funneled through into the different Linear comments or issues in order to consolidate those things in one place. Do y'all still do that or is there a different way that y'all track.

Nan: Yeah, you basically described our process. We have a lot of integrations with things like Zendesk and Intercom. So a lot of the inbound requests and comments and feedback and complaints eventually makes its way to an issue within the system in one way or another. We also do the same kind of thing with the other support channels we have like Slack and we have a pretty active customer community. It's like a dedicated Slack instance. So we're always constantly linking feedback and messages from those channels into Linear issues as well.

Jacob: Does the feedback have any sort of upvote mechanism within the issue itself in order to move something up the chain of priority? Or is priority kind of a manual process that's based off gut instinct from the team and you just use the feedback to inform that process? How does that work?

Nan: It's probably closer to the latter. If something keeps coming up over and over again and a list of associated customer comments for a particular issue gets huge, it becomes fairly obvious, but it's kind of binary in that way, right?

Either it's there to inform "hey when we do get around to building this feature or to making this improvement or whatever it is, let's make sure we've taken into consideration everything that we've heard. Or, it’s like, "so many people are talking about this, we really ought to think about prioritizing it."

So it's binary. There's no “sort everything by number of times it's been voted by people” or anything like that. It's like a two step process.

How does Linear think about the role of AI?

Jacob: I want to wrap up on a question that is very relevant to today's larger conversation around AI. I've seen some AI tools come out for product managers that seem to try to automate or take some of the grunt work out of general PM responsibilities. And Linear is already doing that without AI.

And so I'm really curious about the crossover because we're getting to a point where, with GPT-4 and other models, there's a lot that AI can do in a way that's very helpful for getting the project management part out of the way and letting people focus on actually building.

How are y'all thinking about AI and what do you think that the role of it is in the context of Linear?

Nan: We're definitely thinking about it a lot. Jori is very hands on wit thinking about our AI strategy and how we want to apply that to the product and to the business.

On a theoretical level, the first thing we have to acknowledge is that things are moving extremely quickly and any assumptions about what it's capable of doing and what it's not capable of doing can just be completely made obsolete in like a few months. So I think we have to go into it with some level of self skepticism and sort of humility about that.

1. Natural Language Queries

But the places where we can really use some of these large language models in a way that's naturally helpful for people is, if you think about a lot of the interfaces in Linear, what makes it so powerful is we give people ways to do visual programming in terms of building entry points into their issue database, right?

Like what are different ways I can look at this collection of issues, whether it's through Insights, whether it's through custom views or filters on projects and things like that. Those are all kind of visual programming interfaces. And a thing that AIs are good at right now is translating natural language into sort of structured language, right?

So I think there's a lot of opportunity to give people easier ways to construct those queries. People are like, “I got my AI to write sequel for me.” It's basically the same thing. 

2. Semantic Issue Processing

I think if you go one step beyond that and you can break apart issues down to their sort of semantic concept, you can do stuff like automated de-duplication much better than we're doing today.

Every day you can think of your product a little differently. You're like, "hey, there's a feature taxonomy that's structured in a certain way or maybe I can think of it from the perspective of different user personas or things like that."

And I can try to break apart the semantics of the issue to say, "organize all of the issues in this area by these five different user personas?" And that's something where like previously, what would you have to do? You would have to, at the time where you create each issue, spend some mental effort to tag it according to some dimension that maybe you'll use later, right?

There's a lot of this like just-in-case type of admin work that you have to do. And I think one of the most powerful things that AI opens up the possibility for is turning that just-in-case work into just-in-time work, right? I have a new idea. Maybe there's a fifth user persona that we weren't considering. Now, can we retag every issue according to this new taxonomy? And previously that would just be like the most annoying thing ever to do in the universe, right? And now it's like, “just let a computer take a first pass at it,” right? And then we can just see if there's something that makes sense.

So I think those are near term, obvious opportunities that take advantage of what AIs are good at today. But I don't know what they're gonna be good at in six months. That's the interesting and scary and fun part of the state of technology right now.

I hope you guys have enjoyed this interview as much as I did. If you enjoyed it, please share it so others can learn from world-class builders.

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I recently watched the first episode of the new series on Netflix called "New Amsterdam" that follows the story of the new Medical Director at New Amsterdam Hospital in New York.

His first day on the job, he keeps asking one simple question.

"How can I help?"

It hit home with me because lately I've been brewing on the complexities of product management at an early-stage company and it can get complicated fast. What are the best tools for user research, the best ways to slice and dice the data, the best design patterns, the best prioritization frameworks, the best pricing psychology, etc.

But this simple, 4-word question does a perfect job of summing it all up.

How can I help?

This short article is for the product-focused founders and early PMs at early-stage startups. It seems as though Product Management has become this black hole of frameworks. And while those things are immensely helpful, I think they've tended to shape the role for newer PMs toward this data-driven, framework-powered robot that's unable to make creative decisions based on gut instinct.

On the latest episode of Lenny's Podcast, Casey Winters makes a similar observation.

"Every new person on the product team is acting like they work at Google and have these infinite resources and infinite time to make sure everything is perfect. It became such this focus on the right way of doing product management that no one's taken any risk. […] At Reforge, we're building frameworks that are tools in a toolkit. You pull them out when relevant. They're not a coloring book to stay inside the lines of."

—Casey Winters, former CPO at Eventbrite

This focus on product management as more of an art form and less of a science was the driving force behind this newsletter in the first place. That's why I named it "Making Product Sense" - product sense seems to be a bit of a lost skill in the last few years, pushed out by the ever-growing list of frameworks.

For the PMs at early-stage companies, product sense becomes increasingly important.

Early-stage companies don't have the luxury of capital. Even if you're lucky enough to raise a round or two, you probably are still hunting for Product-Market Fit which will make it difficult to raise the third round. Plus, you're contending with potential pivots, global catastrophes like COVID, the volatility of the market, aggressive competitors and more, all of which could kill you in an instant.

Early-stage companies don't have the luxury of time. Because you don't have capital, you also don't have time either. Until you're profitable, you're on a constant death march toward running out of money.

Early-stage companies don't have the luxury of data. Because you don't have capital or time, you don't really have data either (or at least not much of it). Because until you have the money to hire a data scientist or time to dedicate to building data infrastructure, there will almost always be things that are a higher priority.

So... that begs the question, how do you stop wasting money, move fast, trust your gut and still make good decisions?

The Role of Founding Product

First, let's clarify the role of a PM at an early-stage company. It's not to build a really beautiful prioritization system in Asana. It's not to run impressive SQL queries. It's not to build presentation decks on your strategy.

To quote Gibson Biddle, your job is to “delight customers, in hard-to-copy, margin-enhancing ways.”

Breaking that down, how are you:

1. Adding value to customers...

2. In ways that adds value to the business.

Frameworks are great, but frameworks have a way of getting in the way. They distract you from your most important role as a PM which is figuring out the best ways to serve your customers.

So how do we do this?

Ask your customers "how can I help?"

The best source of data is your customers. As a founder or early PM, you should be talking to customers every single day to keep a pulse on their needs as well as their reactions to your product in real time.

The second best source of data is your sales and support people. Sales people are getting a great pulse on user needs and support people are getting a great pulse on customer reactions to the product. In our case, I run support and my co-founder runs sales so we're on the phone with users literally every single day. But if you're not in either of those roles, make sure you're talking with the folks who are because they're getting the most raw, intimate data possible.

In a recent conversation with Linear’s Head of Product (coming soon), I was reminded that these conversations are often riddled with biases. A good reminder to try not to lead your customers toward an outcome you want nor to take their feedback as gospel. There’s a balance that’s required which goes back to good ol’ fashioned product sense.

Ship first, then learn.

I say this a bit tongue-in-cheek because if you have data to learn from, use it to make educated guesses. But don't spend weeks or months doing research and not shipping.

Practically, shipping first doesn't have to be production-ready code. You can use no-code tools to spin up a landing page or an MVP of a feature or even just mock something up and send it to a user to get their feedback. There are multiple features in our product today that are 100% no-code giving us the ability to test user needs and demand and eventually, if we believe it's worth it, they'll be integrated into the product.

That's it. There's a temptation to over-complicate it but if you're learning from customers and shipping, you're on the right track.

Here’s to the builders 🍻

That’s all for this one - I’ll catch ya next week.

—Jacob ✌️

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Two weeks ago, I watched with millions of other gamers as Fortnite released Chapter 4: Season 2. If you aren't familiar with Fortnite, it's one of the most popular games in the world with over 500 million players. It popularized the “Gaming as a Service” (GaaS) era that monetized the game post-release by offering the game for free and selling cosmetic upgrades to the tune of $1.2 billion in their first year.

They also popularized the cyclical release model, dropping updates to the game every few months, rather than the traditional model of releasing a new editions every year (eg. Call of Duty), which brings me back to my story. Season 2 was the latest Fortnite update and it was nothing short of impressive. I was blown away by the impressive skyscrapers and forests, unique weapons and augments and ever-creative ways to move around the map. Every season, they somehow manage to outdo themselves.

And while the game itself is an incredible feat of engineering, game design and business strategy, a few days ago (on my birthday coincidentally), the team at Epic Games dropped what might be the biggest announcement in the history of Fortnite.

Unreal Editor for Fortnite (UEFN)

This announcement will certainly have an enormous impact on Fortnite itself, but I predict that it’ll also change the landscape of the Metaverse as well, possibly even forcing Apple’s hand.

Let's get into it.

Unreal Engine

Fornite (along with hundreds of other popular games) is powered by a gaming engine built by their parent company, Epic Games, called Unreal Engine. It's "the world's most advanced real-time 3D creation tool for photoreal visuals and immersive experiences."

With the release of Unreal Engine 5 back in April 2022, the graphics have gotten incredibly realistic. So much so, that Industrial Light and Magic even used UE5 to generate lifelike, dynamic scenes on their StageCraft wall when they filmed The Mandelorian allowing them to change lighting and angles in real time with the camera movements and actions happening in the scenes.

Epic offers the power of Unreal Engine to other game developers as well, allowing them to import, download or even create their own 3D models, animate them, add realistic textures, play with dynamic lighting and more.

But while independent game developers can use Epic's tools to build their own games and even sell those games in Epic's game store, the worlds themselves are still disconnected. If I want to play multiple games, I have to create multiple accounts.

Cross-Game Interoperability

If I log into Gears of War and pick up a Tripwire Crossbow, I can't take that weapon into Tomb Raider. Or if I earn Creds in Valorant, I can't use them to buy a Battle-Car in Rocket League.

But… what if you could?

What if there was a greater universe where all of these games existed like the Oasis in Ready Player One? Where the items you earn from a quest on one world can be used in a battle in the next? Or where in-game currencies convert to a standardized credit system across the universe?

That's where Unreal Editor for Fortnite comes in.

UEFN gives game creators the ability to build other worlds WITHIN the Fortnite universe, allowing you to traverse the various worlds with your Fortnite avatar.

Whether you like the surreal…

The realistic…

Or something totally different…

The options will be endless.

Having a persistent in-game identity is an enormous step toward the greater vision of the metaverse. Some worlds may restrict or alter your skin, abilities, items, and currencies and others may opt to use those assets cross-world, but in either case your in-game identity stays the same.

We see a version of this in Meta's Quest platform as well where your Meta account acts as your in-game identity across games and worlds which begs the question, how is Fortnite any different? And, more importantly, how will Fortnite win?

How Fortnite Will Win

As I wrote in this article about Meta's pivot toward the Metaverse, there are a few things that are required for the Metaverse to exist:

• Infrastructure: an interoperable ecosystem

• Objects: skins, items and digital property

• Worlds: places for players to visit

• Experiences: not just places to go, but things to do

• Hardware: the gear that allows you to interact with the meta dimension

But you know what? I think I made a mistake here. I defined the hardware as VR headsets, haptic suits and immersion rigs. But I actually think the hardware doesn't have to be quite so immersive. After all, humans have been getting lost in 2D experiences for decades from games like World of Warcraft to alternate realities like Second Life. So why should the metaverse be defined so narrowly as to only include virtual reality? The metaverse probably will start out in 2D (ie. laptops and phones) before moving to 3D (VR/AR).

If we expand that definition, it immediately becomes clear how Epic can win the metaverse race.

• Infrastructure: Unreal Engine

• Objects: skins, gliders, pickaxes, V-bucks and now, an entirely new suite of objects that game developers can create with UEFN

• Worlds and Experiences: game developers can now build worlds and experiences within Fortnite

• Hardware: your laptop, console, mobile phone and, eventually, headset (we’ll talk more about this in a bit)

Ultimately, I believe it shakes down to two concepts that I've written about before:

1. Means of Creation: Epic's infrastructure to build the in-game objects, worlds and experiences is leading the way in technological capabilities as well as openness and interoperability. Meta, on the other hand, doesn't offer a game engine. In fact, if you were to build a game for Meta's Quest headset, you'd end up using a game engine like Unreal Engine.

2. Means of Distribution: And on top of that, if you're a game developer and you want to get your game in the hands of as many players as quickly as possible, do you build for Fortnite's 500 million players across all platforms or Meta's 5 million users for Quest headsets only? It’s a no-brainer.

Epic Games has had the dominos lined up for a while and with the release of UEFN, they have tipped over the first one that I predict will cascade into an industry-defining ecosystem being built within Fortnite.

In Epic's demo of UEFN last week, they not only showed off some fantastic battle games set in different worlds, but also a really cool escape room showing that, despite Fortnite’s history as a Battle Royale, many different types of experiences can be created within Fortnite. In time, I believe we'll see more worlds that are NOT battle games than worlds that are. Worlds like…

• Co-working spaces with other users

• Virtual malls with realistically rendered products that you can purchase and have shipped to your real-world home

• Experience simulations like put put, flying an airplane, or surfing a monster wave

• Recreations of real-world places like museums, monuments or tourist hot spots like Time Square

Let’s wrap this up with a definitively far-fetched prediction.

Forcing Apple’s Hand

Apple has never been known for their games. Their computers just couldn’t handle the intensive processing power required for gaming… until now. With their M-series chips, they can handle the load, especially if those games were designed specifically for Apple Silicon.

Apple is rumored to be dropping a new product soon: their take on a VR headset. And so far, one of the best applications of a headset is gaming - the very thing Apple can now handle but still isn’t great at. If Apple wants to get a leg up on Meta, they need to give their customer base a reason to buy their new headsets. Like a hit game.

Fortnite, for their part, has been rumored to be working on a first-person mode, making the game even more immersive and potentially able to be adapted for VR. The problem is that they don’t make hardware so they’re going to have to find a platform to build for. The most obvious would be the Meta Quest since that’s one of the more advanced headsets on the market.

Here’s my take: Apple and Epic Games should squash their beef and team up.

Apple can provide Epic with a headset play to truly be cross-platform and Epic can provide Apple with a gaming play for their headset.

Just my two cents. I think it could be cool :)

I’d love to hear your thoughts! Drop ‘em in the comments.

Leave a comment

That’s all for this one - I’ll catch ya next week.

—Jacob ✌️

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This past weekend I was traveling to the PNW and had a couple hours to kill at the AirBnB Saturday morning.

I had been watching the GPT-4 hype unfold on Twitter since Tuesday and even collected a list of the 10 coolest ways folks were using it and decided it would be fun to put it to the test myself. So I upgraded to ChatGPT Pro ($20/m) which let me access GPT-4 and got to work.

My goal was simple: build an iOS application.

I’ve build React/Node web apps in the past but I wanted to use a language I had never learned before so I could handicap myself and really see what GPT-4 could do.

Let’s get into it.

Since my wife had just started her own newsletter (Subscribe → Functional Medicine Weekly), I decided to build a simple app that would store supplement protocols for chronic illnesses.

My first prompt just described the basic functionality of the application. I didn’t go into too much detail because I honestly didn’t know what I wanted the app to become yet. I just wanted to get started.

Here’s what it said.

One thing that was really beautiful to me from the get-go was that it broke down my rambling prompt into a clear plan of action and then it began to write the code. I copied and pasted the code it generated into the files it told me to. Simple enough.

Along the way, as I began to ask it to make edits, I would ask clarifying questions like this one…

…and drop in some errors I was running into like this one.

Like I said, I didn’t know a lick of Swift so I was relying on ChatGPT to be as clear as possible.

But as I started to get the hang of some of Swift’s semantics, I felt like I could implement the updates myself on a per-line basis without having to rely on ChatGPT to re-generate an entire file at a time for simple copy-and-pasting. So I asked it to just give me the code that needed to be changed and nothing else.

The whole thing took me maybe an hour and a half.

1. Within 5 minutes I had an initial build running in the X-code simulator.

2. Within 1 hour I had implemented a few additional features and troubleshooted a handful of bugs.

3. Within 1.5 hours I had cleaned up the UI a bit and made some adjustments to make the user experience a little cleaner.

Here’s a short clip of the app after about 10 minutes.

A few things worth noting:

• I didn’t hook up to an API

• I didn’t set up a database

• I didn’t use any of the phone’s hardware (GPS, gyroscope, accelerometer etc.)

• I didn’t even leverage GPT-4’s powerful multi-media capabilities (I don’t have access to their API yet)

I’m quite sure that someone who knows Swift could have coded the whole thing in the same amount of time that it took me to write the prompts and copy and paste it over. Or use ChatGPT to build an even cooler version of the app in the same amount of time.

But the fact that I was able to build a simple Swift app with no prior knowledge of Swift is impressive, dare I say mind blowing. Which brings me to the larger point here.

The rate at which builders can build just rose dramatically.

When Mark Andreessen wrote “it’s time to build” back in 2020, I don’t think he anticipated this incredible advancement 3 years later but I guarantee you he’s stoked out of his mind right now. We have an opportunity for more people than ever to bring their imaginations to life. It’s a powerful spin of the flywheel that I hope will spill over from the bits and bytes of software into the atoms of hardware.

That’s all for this one - I’ll catch ya next week.

—Jacob ✌️

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