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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This week on The Glimpse

If you missed it on Tuesday, I wrote about the rise of humanoid robots - our very own C-3POs. Why are humanoid robots so important to our economic future? What are the challenges to overcome? And which companies are best positioned to bring this future to life? I think you’ll enjoy this deep dive into the world of humanoid robots!

🔮 Glimpse #1 — The Galactic Gas Station

“It could look something like this: in partnership with the US government, a private company — let’s call it Regolift MegaCorp — sends fuel-cell powered, tele-operated robots to the lunar poles to mine their icy regolith. There, they separate the ice from the soil, and electrolyze the ice to make hydrogen and oxygen, which when condensed into liquid form (LOX, LH2 respectively), can be used as propellent. It’s potentially most valuable (useful) in orbit, so Regolift MegaCorp launches the propellant off the moon to an orbital outpost where clients can access it.”

—White Pill, referencing this paper by Philip T. Metzger

🔮 Glimpse #2 — A history of genetic design

Our ability to design biological systems is improving rapidly. In 40 years, the field went from modeling three genes in phage to building synthetic gene circuits with 76,000 bases of DNA. A brief history of genetic design, told in 7 experiments.

—Asimov

🔮 Glimpse #3 — How a pistol shrimp inspired developments in nuclear fusion

“The shrimp has one outsized claw that it can snap really fast to make a shockwave strong enough to stun its prey. This also produces bubbles,” Hawker explains. “As the shockwave passes over the bubbles, it makes them collapse, which releases energy in the form of bright flashes of light. A big part of my PhD was studying that process of cavity collapse with fusion in mind. I wondered whether it could be applied to generate power. The answer was ‘probably not’, but it did help us to set up simulations to see how the principle could be adapted for use in fusion.”

—Dr. Nicholas Hawker, Raconteur

🔮 Glimpse #4 — Infrastructure for Web3 Communities

“The future of decentralised collaboration and governance is here. The Jur Society dApp Beta is now live and we are whitelisting the very first founders.”

—Jur Society

🔮 Glimpse #5 — Interview with Varda Space founders

Varda is building the flywheel of space manufacturing. Listen in!

Hope that gave you a little inspiration and wonder as you head into the weekend :)

—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 week on The Glimpse

I didn’t release an article this past Tuesday because I wasn’t happy with it. I only want to release articles that are thought-provoking, inspirational, and educational. If I can’t accomplish those three things, I don’t want to clog up your inbox.

That being said, be on the lookout for an article dropping this Tuesday on the rapidly-advancing future of humanoid robots (aka C-3PO 👀).

Without further ado, here are five Glimpses of the future 🔮

🔮 Glimpse #1 — LLNL achieved Nuclear Fusion Ignition… again! And with higher yield.

Researchers have produced fusion reactions before, but it has taken more energy to cause the reaction than they could get back. The key thing about these last two experiments is that they get more energy back than they put in to create the reaction. That efficiency has been the elusive holy grail of fusion research.

—Washington Post article

🔮 Glimpse #2 — A bacterium can suppress the spread of malaria in mosquitoes

Scientists found a natural bacterium (Delftia tsuruhatensis TC1) in mosquitoes that can't carry the parasite causing malaria. This bacterium produces a substance called harmane that stops the malaria parasite from growing. Using this bacterium could be a new way to help control malaria by targeting the mosquitoes that spread it.

Link to the Science.org article

🔮 Glimpse #3 — A maneuverable, back-up satellite can be tasked to bring broadband connectivity to underserved areas

The real utility comes from the satellite’s dual-propulsion chemical system and electric ion thruster. “That allows us to have this incredibly powerful asset we can bring to bear in almost any kind of situation you can think of where someone needs broadband connectivity in a remote or very underserved location,” Gedmark said.

One such situation could be during a natural disaster, when reliable communication is paramount; another might be national security, to add resiliency to space-based and ground communication architectures.

—TechCrunch article

🔮 Glimpse #4 — AI and robotics are being used to automate the rapid discovery and testing of antibodies that humans would have never tried

“The only input you give the system as a human is, here’s an example of a healthy cell, here’s an example of a diseased cell,” says Field. The model selects more than 700 initial options from across a search space of 100,000 potential antibodies, and then automatically designs, builds, and tests them, with the aim of finding potentially fruitful areas to investigate in more depth.

—WIRED article

🔮 Glimpse #5 — Evidence of wet and dry seasons on Mars could point to habitability

In the 1990s, NASA satellites orbiting Mars first began to capture gullies, dried-out river deltas, and layered sedimentary deposits, all of which require liquid water to form. These geological features suggest the ancient martian climate was much warmer and wetter than it is today, says Ashwin Vasavada, project scientist for the Curiosity rover at NASA’s Jet Propulsion Laboratory.

—Science.org article (based on Nature article)

Hope that gave you a little inspiration and wonder as you head into the weekend :)

—Jacob ✌️

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This week on The Glimpse

If you missed it on Tuesday, I wrote about how scientists have figured out a way to “reprogram” cancer cells to self-destruct using a natural cell pruning process called apoptosis. It’s a fascinating glimpse into the future of cancer therapies!

🔮 Glimpse #1 — First Light’s inertial fusion reaction could also produce cancer treatments as a by-product without any impact on energy

If you’re curious about nuclear fusion and the energy abundant future companies like First Light are trying to build, read my article on Helion. The future is bright!

🔮 Glimpse #2 — CabinDAO introduces Cabin Weeks as a pilot for their coliving network

🔮 Glimpse #3 — NASA teamed up with IBM to release an AI model of their 250,000 terabytes of satellite data

🔮 Glimpse #4 — 3D printers for rockets

This one’s a bit of a throwback Tweet but this sort of thing really does give you a cool glimpse of the future. This is Relativity Space’s full-sized 3D printer that they use to literally print rockets that they send into space. Check out my article on Relativity if that sort of thing gets your blood pumping like it does mine!

🔮 Glimpse #5 — Hard tech is back baby

Hope that gave you a little inspiration and wonder as you head into the weekend :)

—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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🔮 Glimpse #1 — Scientists from South Korea have discovered room-temperature superconductivity

Here’s the link to the paper. The tweet below is a thread by Andrew Cote that goes further into explaining the impact of this breakthrough.

The team at Varda Space is trying to replicate the room-temperature semiconductor experiment in the Varda “garage” and live streaming/tweeting the whole thing!

And just for funsies because I literally laughed out loud 😂

🔮 Glimpse #2 — StabilityAI releases SDXL 1.0, FreeWilly1 and 2

Stable Diffusion XL 1.0 is Stability’s answer to Midjourney and…

…their FreeWilly models are two new LLMs that are strong, open source competitors to GPT-3 and GPT-4.

🔮 Glimpse #3 — Researchers have found a way to reprogram cancer cells to self-destruct

🔮 Glimpse #4 — Robots for work

🔮 Glimpse #5 — Nasa is working toward nuclear powered rockets within 4 years

If you’ve read my article on Helion and their work in nuclear fusion, you’re familiar with the incredible efficiency of nuclear power. Due to weight constraints, we’re limited on the amount of chemical combustion fuel we can bring with us. But since the efficiency per unit of mass is up to 1,000,000x that of fossil fuels, we can send rockets much farther with less fuel. Mars, here we come!

Hope that gave you a little inspiration and wonder as you head into the weekend :)

—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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This week on The Glimpse

If you missed it on Tuesday, I wrote about startup cities like Próspera that are sanctioned, city-sized countries within other countries. These startup cities are given the license to experiment with governance in a way that we’ve never seen before. What if we could harness those experiments in governance to test new policies before we roll them out to the entire nation?

🔮 Glimpse #1 — Genetic sensors for biosafety

https://twitter.com/Ginkgo/status/1679462432773685248?s=20

🔮 Glimpse #2 — Blockchain human registry

A new blockchain-based human registry just launched that attempts to verify your humanness in a privacy-centric way. I suppose in a world with androids indistinguishable from humans à la Bladerunner, this could useful…

https://twitter.com/joinhmn/status/1679560531655667713?s=20

Meanwhile, their competitor co-founded by Sam Altman (CEO of OpenAI) called Worldcoin just crossed 2M users.

https://twitter.com/worldcoin/status/1679461021650386944?s=20

🔮 Glimpse #3 — Shopify’s AI assistant

ChatGPT caught the world on fire, but I don’t think a general chat bot will be the use case *most* people find themselves interacting with when we think of an AI-powered future. I tend to think it’ll be something more like Shopify’s AI assistant. Deeply integrated, niche AI partners that help you accomplish tasks in context.

https://twitter.com/tobi/status/1679114154756669441?s=20

🔮 Glimpse #4 — The future’s tug of war

The future has an interesting tug of war going on. On the one hand, futurist visions of a clean, safe, natural environment often trend more toward solarpunk.

But on the other hand, cyberpunk dreams are coming to life right now with new construction like The Sphere in Las Vegas.

https://twitter.com/LasVegasLocally/status/1677365165719969792?s=20

Which future do you dream of?

🔮 Glimpse #5 — Beautiful ≠ useful

Great design is raising the “wow” factor for modern apps but… are the apps just pretty or are they also helpful?

https://twitter.com/raffichill/status/1678103335507836929?s=20

Hope that gave you a little inspiration and wonder as you head into the weekend :)

—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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Short note for my readers

You may have noticed - I’ve recently rebranded from Making Product Sense to The Glimpse. As I was writing my “year in review” for the newsletter, I realized that I tended to write about products that were pushing the boundaries of technology. And, according to the poll I put out, those deep dives are your favorite articles as well.

“The future is already here, it’s just not evenly distributed.”

It’s easy to miss the mind-blowing advancements in technology because they tend to exist on the edges. You can think of The Glimpse as your weekly dose of the future. Each week, I’ll continue to write a new deep dive on a company or technology that’s bringing the future to life from AI to AR/VR, space to robotics, manufacturing to synbio.

Most of you are product leaders, founders or investors who, like me, have a vested interest in emerging technology. My hope is that you’ll not only enjoy these explorations but learn something from them that you can apply to your own work as well.

Catch you next week!

—Jacob

If you missed it on Tuesday, I wrote about Ginkgo Bioworks, the platform company for synthetic biology, helping companies design and grow living organisms that can be used for everything from growing fuel, to concrete, to medicine. Yep, you read that right. Companies are literally growing concrete. The future is gonna get weird.

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🔮 Glimpse #1 — Bacteria as a cancer treatment

https://twitter.com/aoifemlbrennan/status/1676603325045788672?s=20

🔮 Glimpse #2 — Share directions with your car

https://twitter.com/Tesla/status/1677102978421989377?s=20

🔮 Glimpse #3 — Drug manufacturing in space

https://twitter.com/VardaSpace/status/1674871004810858496?s=20

🔮 Glimpse #4 — Code Interpreter rolling out for all ChatGPT Plus users

https://twitter.com/OpenAI/status/1677015057316872192?s=20

🔮 Glimpse #5 — On-device analysis for everything you read, watch, or listen to

https://twitter.com/dsiroker/status/1676737991723851776?s=20

Hope that gave you a little inspiration and wonder as you head into the weekend :)

—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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If you missed it on Tuesday, I wrote about a company called Relativity Space that built the world’s largest metal 3D printer and then proceeded to print a rocket and launch it into space. No big deal.

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🔮 Glimpse #1 — Mixed image editing

https://twitter.com/Suhail/status/1674124521543192578?s=20

🔮 Glimpse #2 — Tesla’s Dojo ramps up compute

https://twitter.com/Tesla_AI/status/1671589874686730270?s=20

🔮 Glimpse #3 — Oculus inventor comments on the Apple Vision Pro

https://twitter.com/SadlyItsBradley/status/1672616526556307462?s=20

🔮 Glimpse #4 — Maybe fewer pesticides?

https://twitter.com/HaggertyTechs/status/1584529401932574722?s=20

🔮 Glimpse #5 — 🤯

https://twitter.com/latestinspace/status/1673533901111689218?s=20

Hope that gave you a little inspiration and wonder as you head into the weekend :)

—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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✨ Nug #1 — Latest from Figma’s CONFIG ‘23

https://twitter.com/shkliarau/status/1671905268752343044?s=20

✨ Nug #2 — Google’s new AI search experience

https://twitter.com/rustybrick/status/1656672005356244992?s=20

✨ Nug #3 — Framer AI drew blood

https://twitter.com/zee7/status/1668984326032179202?s=20

✨ Nug #4 — Design-first coding

https://twitter.com/mattrothenberg/status/1669297300286439425?s=20

✨ Nug #5 — Defeated by mayonnaise 😂

https://twitter.com/greg16676935420/status/1669364945224359937?s=20

✨ Bonus Nug #6 — 🤯

https://twitter.com/CopySutra/status/1671944904149438466?s=20

✨ Bonus Nug #7 — You’re welcome

Tired of the threadboi threads? Yeah, we all are. Thank you Josh 🙏

https://twitter.com/Shpigford/status/1670929879095803905?s=20

Hope that gave you a little inspiration and wonder as you head into the weekend :)

—Jacob ✌️

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Wow! Today is the 1-year anniversary of Making Product Sense. Wild to think that it’s been that long.

This is a special edition where I’m going to talk about the good, bad and ugly of writing a weekly newsletter.

Also…

1. Year in Review (+ stats over the last year)

2. Top 5 Articles (+ vote for your favorite type of article)

3. Top 5 Product Nuggets (+ vote whether it stays or goes)

4. The future of MPS (+ vote for the technology that excites you the most)

Don’t worry, I won’t wax poetic - I’m keeping this thing short and sweet with a lot of bullet points.

Let’s get into it!

The Good, Bad & Ugly of Writing a Weekly Newsletter

I’ve learned a lot, I’ve written a lot, I’ve thought a lot. So I’m going to try to be as honest as possible here. What’s it been like writing a weekly newsletter for a year?

🥳 The Good

1. Community: The community is fantastic. I’ve met some truly wonderful people - shoutout to , , and many others who I’ve enjoyed getting to know over the last year.

2. Clarity: Writing is incredibly clarifying. It not only helps me think better but it helps me communicate better in general.

3. Quality: Growth comes through putting in the reps, not from fine tuning a single work until it’s perfect.

4. Consistency: You learn how to ship even when you don’t feel like it, or you have writers block, or you’re sick (like I am today).

5. Creativity: A lot of life isn’t all that creative. It’s really healthy to have an outlet for creative expression because creating something is an innate part of what makes us human.

6. CV: Having a body of work you can point to and say, “I did that,” is the best evidence of your ability to deliver.

😬 The Bad

Now on to the challenges which I’m encouraged to see that there are fewer of than the positives!

1. Mental weight: Most weeks I’m genuinely excited to write. There’s a long list of things that I would love an excuse to research. But sometimes there are days when it feels like a burden. Those weeks are tough.

2. Planning: If my weekend fills up with plans, I have to write in the evenings after work which is very difficult to carve out time for.

👹 The Ugly

I can deal with the occasional mental burden or planning conundrum. But the ones that really make me question if this is worth it are:

1. Imposter Syndrome / The Comparison Game: I often see other writers that I respect who are absolutely crushing it. Twitter threads, Discord communities, complementary podcasts, custom graphics, cross-platform distribution… it honestly can get overwhelming at times and it makes me feel like I’m losing at a game I don’t even want to play.

2. Opportunity Cost: To thoroughly research and write a high-quality article with factual claims, entertaining stories, insightful or educational takeaways and a unique hook takes a LOT of time. Time that I could be spending with my wife, playing sports with my friends, doing work around the house or even just resting.

Share

Do you write a newsletter too? Drop a link in the comments with the good, bad and/or ugly from your experience!

📆 The Year in Stats

A few stats from the last year.

• 49 long-form articles

• 101,500 words (This is not an estimate! I actually landed on a round number 🤯)

• ~3,500 subscribers

• ~187,000 views

Thank you all so much for reading, commenting and sharing - it truly makes this newsletter a life-giving part of my week :)

Here are the most popular articles from the last year 👇

Here’s what’s fascinating to me. As this newsletter has evolved over the last 365 days, I’ve experimented with different types of articles. Surprisingly, at least one of these different types made it into my top five, approximately one every other month 😂

So let’s settle this. What’s your favorite article type?

🐤 Product Nuggets

Product Nuggets was my Friday roundup of my favorite Tweets from the past week. It was a way to share exciting, interesting or funny things happening in the world of tech and product that I didn’t dedicate a full article to.

Here are the most popular Product Nugget roundups 👇

👋 What about you?

I’m so thankful for everyone who reads this newsletter. I’d love to know more about y’all! What gets you excited? What do you do? There are 4 short polls below :)

That’s all for this one - I’ll catch ya next week as we kick off year two of Making Product Sense!

—Jacob ✌️

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✨ Nug #1 — A.I. “Uncrop

https://twitter.com/StabilityAI/status/1666843691233820674?s=20

✨ Nug #2 — A.I. Doctor’s Assistant

https://twitter.com/rowancheung/status/1665755309670019072?s=20

✨ Nug #3 — A.I. Chef

https://twitter.com/aakashg0/status/1666301809768677376?s=20

✨ Nug #4 — A.I. ETF

https://twitter.com/MikeZaccardi/status/1667192281730891776?s=20

Disclaimer: This is not investment advice. Do your own research.

✨ Nug #5 — A.I. Prompts

https://twitter.com/raycastapp/status/1667157106192183298?s=2

Hope that gave you a little inspiration and wonder as you head into the weekend :)

—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!

Leave a comment

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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✨ Nug #1 — Chat with AI NPCs in video games

https://twitter.com/hexeract01/status/1663221285210447872?s=20

✨ Nug #2 — Data-to-Text-to-Image

https://twitter.com/BjoernKarmann/status/1663496103998750721?s=20

✨ Nug #3 — ChatGPT’s new HoP is a baller

https://twitter.com/aakashg0/status/1664095121304870914?s=20

✨ Nug #4 — Turn 2D video into 3D structures

https://twitter.com/NVIDIAAIDev/status/1664264637053911041?s=20

✨ Nug #5 — So meta

https://twitter.com/spacecasetay/status/1663927112913354752?s=20

Hope that gave you a little inspiration and wonder as you head into the weekend :)

—Jacob ✌️

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If you enjoyed this edition of A.I. Nuggets, smash that heart icon! 🙏