In this roundup:

•  A.I.
  • Essay: Thoughts on the AI epoch — An idea maze for LLMs; Punctuated Equilibrium; The AI revolution; Where’s the moat?; The fate of Google.
  • My thoughts on AI (as a podcast!)
  • The BuffettBot Experiment
•  Space — 4 photos and a link to summarize 2022.
•  Interesting Links — Other takes on AI; Derek Thompson essays; and Choosing Good Quests.

 A.I.

Thoughts on the AI epoch

What more can be said about the AI boom that began its ascent less than a year ago? A lot! The potential of AI is immense and its influence on our lives is sure to be significant. And so I’ll continue. . .

Continue reading…

My thoughts on AI (as a podcast!)

A few months ago I had a discussion on my friend Eric Jorgenson’s podcast. We intended to talk about a variety of topics, but ended up discussing mostly AI.

With how fast progress has been moving recently, two months ago is like years, so some of the content may already be out of date. Some topics we discuss:

What is computer vision? How does a neural network work? How we’re making the world legible to computers. What will AI be capable of? How will Google fare in this new paradigm? Can computers smell?

#051 AI Evolution: ComputerVision, Olfactory Computation, and Neural Nets with Max Olson

The BuffettBot experiment

On December 14, I launched BuffettBot.com.

BuffettBot uses the OpenAI API to search an archive of his writings and ask “Warren Buffett” questions. It even connects the answer to quotes and sources it pulled the information from.

Unfortunately the service only ran for 3 days before I had to FLIP THE KILL SWITCH.

No, BuffettBot didn’t become sentient and threaten to allocate the world’s resources. The usage just exploded and I couldn’t justify keeping it running given the daily cost of using the API.

Ultimately even though I had to shut it down for now, this was a successful experiment.

What’s the future of BuffettBot?

Given the cost to run it in the current form, I’m not sure what the future holds. I don’t think the best model for something like this is pay-per-use. (Although a platform with many of these bots may justify something like “charge $10 to ask 100 questions”.)

A version that’s much more cost efficient and still very valuable is something like the following:

“Buffett Archive” where you can semantically search all of Warren Buffett’s writings and interviews. Semantic search means you’re not doing a ⌘F and looking for an exact phrase, you’re looking for meaning in the text. So you could search “how did GEICO’s float change in 1995” or “the Washington Post’s moat” and get the most relevant results. The app will allow you to browse all the results, showing them in context of the letter/transcript/etc. that they were found.

Let me know if you’d be interested in something like this! Given the popularity of BuffettBot, I think this would be pretty valuable. And a “Munger Archive” as well.

 Space

Although AI progress in 2022 seemed like the main show, we made some pretty big leaps forward in space.

The past year can be best summarized in 4 photos:

For a more detailed summary of what went down in space last year, Orbital Index gave a great rundown here.

 Interesting Links

• Other good AI takes I’m fond of:
  • Before the flood, by Samuel Hammond
  • AI from Superintelligence to ChatGPT, by Séb Krier (Works in Progress)
  • The Age of Industrialized AI, by Daniel Jeffries
  • Superhistory, Not Superintelligence, by Venkatesh Rao
• Derek Thompson has written a few good essays recently:
  • Why America Doesn’t Build What It Invents — The U.S. just made a breakthrough in nuclear-fusion technology. Will we know how to use it?
  • Why the Age of American Progress Ended — Invention alone can’t change the world; what matters is what happens next.
  • Your Creativity Won’t Save Your Job from AI — Robots were once considered capable only of unimaginative, routine work. Today they write articles and create award-winning art.
• Unblocking Abundance, by Sarah Constantin — What can we do to remove to barriers to progress?
• Choose Good Quests, by Trae Stephens and Markie Wagner — Silicon Valley’s current focus on easy money has resulted in a failure to solve big problems. There is a moral imperative for our best players to choose good, hard quests, which will make the future better than the world today.

In this essay I’ll focus more on Large Language Models (LLMs), but my thoughts apply to all other AI efforts as well.

An easy way to think of it is that LLMs will soon become the “autocomplete for everything”:

What’s common to all of these visions is something we call the “sandwich” workflow. This is a three-step process. First, a human has a creative impulse, and gives the AI a prompt. The AI then generates a menu of options. The human then chooses an option, edits it, and adds any touches they like.

. . . So that’s our prediction for the near-term future of generative AI – not something that replaces humans, but something that gives them superpowers. A proverbial bicycle for the mind. Adjusting to those new superpowers will be a long, difficult trial-and-error process for both workers and companies, but as with the advent of machine tools and robots and word processors, we suspect that the final outcome will be better for most human workers than what currently exists.

There seems to be an unlimited number of areas that language prompting + completion will enable. Some are obvious: a new iteration of Google, help with writing, content generation, help with marketing copy, etc. You see many startups and tools that have already sprung up to tackle these.

Some of the real interesting applications that are incubating now will have action models as a big component. Models will have the ability to take actions like: searching the web; ordering an item; making a reservation; using a calculator; or using any other digital tool that humans are capable of using. Imagine ChatGPT being able to confirm its answers with multiple sources, or having access to all your personal records it can use to assist you.

Prompt engineers are already discovering how much you can do with the existing models, without any new advancements or manipulation of the actual base model. Even if GPT-4 or an open-source LLM from Stability.ai take years to come out, the existing tools are enough for huge changes.

An idea maze for LLMs

The above is an idea maze I sketched out for products enabled by LLMs. The key question it starts with is “What kind of interface would the use have with the product?”

• Search — you search for something just like you do on Google, but the results are much more relevant to what you’re truly looking for.
• Chat — you’re having a conversation with the AI, and as part of that back-and-forth communication it helps you accomplish some goal.
• Copilot — the AI “sits beside you” in whatever you’re working on, assisting where it can and giving whatever you’d normally do a big productivity boost. The actual interface in this case would be highly specific to the use case.
• Content generation — you give the AI some minor guidance upfront, and it generates the entire content to use (this might be considered a Copilot interface).
• [Games] — similar to chat, but you’re not necessarily having a conversation. The AI is leading you or enabling some sort of game-like interface for entertainment, learning, or problem solving.

A few highlighted examples:

• Personal assistant — Imagine Siri or Alexa if they actually had some serious capabilities. You say “My wife and I want to take a trip to Europe this summer,” and it uses travel booking APIs and TripAdvisor to create a handful of itineraries, their prices, and best aspects of each. You pick one, and it makes all the reservations for you. It follows up with reminders and suggestions on what to pack.
• Legal copilot — You’re a lawyer at a law firm. A LegalAI tool you use knows every law in existence, along with every prior case and their outcomes. You use it to pull out the most important points of a 200 page contract, and summarize it for your client. You use it to draft an agreement by listing the most important considerations, and it suggests points you may have forgot. Most impressively, you’re representing your client in a corporate fraud case and after ingesting all the relevant details, LegalAI suggests ways to win the case, including esoteric legal loopholes and creative arguments to sway the judge. The digital version of Extraordinary Attorney Woo.
• Personal tutor — You’re a teacher or parent of a child in 5th grade. They’re struggling and falling behind in math. You talk to an AI tutor service first to provide an overview of the situation, just like you would a real tutor. The child speaks with the AI, and it finds out more about what the know and how they learn best. It does daily sessions with them over the course of a few months, and they are now caught up with the rest of the students. Kids who would have never been able to afford a private tutor in the past now have the most knowledgeable tutors in the world. They still can’t fully replicate in-person tutors, but for most kids this doesn’t matter.

Punctuated equilibrium: Welcome to the AI revolution

The study of ecology can provide many good analogies to the world of business and technology. Both are complex adaptive systems, with structures and behaviors emerging over time as they evolve. Both have building blocks that combine into many variations as they adapt to fit their changing environment.

One of these models is punctuated equilibrium.

Punctuated equilibrium is the theory that species evolve at a slow, steady rate for long periods of time, with occasional, rapid changes taking place over short periods of time. The idea is that evolution progresses in a series of short bursts of rapid change, followed by long periods of stasis. These changes play out more like S-curves or step-functions then smooth linear or exponential growth.

It’s very clear that the current AI epoch is punctuation event in the history of technology and business — a rapid evolutionary change during which the world evolves quickly in response to changing environmental conditions.

It’s on the level of other computing revolutions like smartphones, the internet, and personal computers.

Microprocessors and other computer hardware led to cheap PCs in the hands of businesses and individuals. This allowed them to use software, the real general purpose productivity booster. Software allowed businesses to automate and streamline many of their operations, and individuals to do things they never thought possible before.

But software can be clunky. It still takes a lot of effort and maintenance to solve problems using software, and it is really bad at tasks that involve reasoning, learning from data or making decisions.

Enter AI models — Software 2.0:

Software 1.0 is code we write. Software 2.0 is code written by the optimization based on an evaluation criterion (such as “classify this training data correctly”). It is likely that any setting where the program is not obvious but one can repeatedly evaluate the performance of it (e.g. — did you classify some images correctly? do you win games of Go?) will be subject to this transition, because the optimization can find much better code than what a human can write.

AI can take in data, learn from it and make decisions — just like humans can. This means it can automate tasks that are difficult or impossible to do with software alone. AI is opening up new possibilities and opportunities that weren’t available before.

I’ve seen some comparisons, both negative and positive, to crypto/web3 hype in 2018 or 2020. The comparison isn’t apt to me at all. Here’s a few reasons why:

1. With the internet or crypto, you needed other people to be involved to make it more useful. The current crop of AI is useful out of the box and doesn’t need network effects, similar to PCs. (Although in both cases it still helps to have more people using in the long run.)
2. Web3 is still a solution looking for a problem. It will have legitimate uses (you can make a good argument that AI will actually drive those uses in some cases), but there are much clearer products or features that can have immediate value to users for AI. See my experiment with BuffettBot — I built this in a week and within a day thousands of people got immediate use from it!

NZS Capital’s Brad Slingerlend phrased the potential effects well:

Could we see a doubling of productivity across nearly every information-based job? It’s such early days, yet the results are so promising, that I am willing to venture into the extremely dangerous territory of making predictions – and declare that we just might see massive productivity increases from chatbots and generative AI unlike anything we have yet seen over the course of the Information Age – outweighing even PCs, smartphones, and the Internet. I hate the expression “buckle up”, but it might be called for here.

Where’s the moat?

Continuing the comparison to past computing revolutions: I believe it’s likely that, relative to the internet or PCs, this epoch of AI may be less disruptive to incumbents and more productivity-boosting to humanity.

It doesn’t have built-in network effects or economies of scale like the PC app ecosystem or the internet. Nevertheless, I think competitive advantage can be strong depending on the area:

• Technical — In some niche areas, model structure and implementation may give an advantage for long enough to put the product in some other defensible flywheel. They don’t even need to be better quality either. Making models faster, cheaper to run, or available offline can give distinct advantages in some areas.
• Learning curves & data network effects — If model uses Reinforcement Learning from Human Feedback (RLHF), the more people that use it, the better the model becomes. The more niche the use case, the more important this becomes.
• Switching costs — Many will have strong switching costs due to: (1) tight integration with workflow, (2) personal attachment (like a personal assistant), or (3) regular/habitual usage associated with interface or brand.

The last point about regular usage associated with an interface may be key:

Any company that can control the user’s main interface will win.

Scott Belsky, the CPO of Adobe, has a great theory about this:

Companies that successfully aggregate multiple services in a single interface have a chance of really shaking up industries. As soon as you rely on one interface for a suite of needs, you become loyal to the interface rather than the individual services it provides.

Where will users interact with the AI model? If it’s easier for people to use it through an existing, known interface, it’s much easier for incumbents to adapt and add AI as a feature. Startups had an advantage in the internet era because the interface was completely new in most cases. It was disruptive (in a Christensen sense) to their business models.

It’s clear that AI writing assistants will be big. But who will win here? If Microsoft Word, Notion, and other existing apps can integrate AI properly within some reasonable time, most users would rather stay then move to a new, unfamiliar interface. AI seems more like a sustaining innovation in this case.

Which brings us to the big one: Google.

If you ask the same question about Google, the next question becomes “how long will it take for Google to integrate LLMs into their main search UI?” And if in the next few years they can do this just enough that people don’t go elsewhere for general-purpose queries, then users will stay.

The question after that is what it will do to Google’s business model. Is it still sustaining? Possibly. Even if so, it could make it not as financially lucrative.

Liberty shared some good thoughts on how Google will be impacted here [paid]. A good scenario for them may be that their gross margins only go down a bit as search costs increase.

Just before publishing this, Ben Thompson of Stratechery wrote a great essay that essentially aligned with my thoughts above. The most relevant paragraph:

What is notable about this history is that the supposition I stated above isn’t quite right; disruptive innovations do consistently come from new entrants in a market, but those new entrants aren’t necessarily startups: some of the biggest winners in previous tech epochs have been existing companies leveraging their current business to move into a new space. At the same time, the other tenets of Christensen’s theory hold: Microsoft struggled with mobile because it was disruptive, but SaaS was ultimately sustaining because its business model was already aligned.

In the years since launch, it has given location managers the ability to customize their menus in ways they were unable to in the past. This empowers them to make frequent changes, tailoring the menu to customer needs rather than just “using the default”.

Mashgin Creator is a tool for operators to build and manage their menus, from items to discounts, schedules, and more.

Mashgin customers have been able to easily edit their checkout items in the cloud since we first launched in 2016. But when we began to design our mobile and in-person ordering app, we realized customers would need an easy way to design more complex menus, with custom item options, photos, nested categories, scheduling, and more. This is where the idea for Creator came in.

Creator is what they call in the industry a “CMS”, or content management system. Any software tool used to manage content of any type could apply.

In the food service industry, a CMS is used to manage their menu items, pricing, discounts, taxes, etc. The scope could be anywhere from an individual cafe to a nationwide chain of stores.

Most existing CMS software for food service was cumbersome to use and poorly designed. It was really just a simple layer on top of a database, allowing users to edit basic item information. Some software didn’t even allow for real-time syncing of data — any changes are “submitted” and someone behind the scenes has to deploy them to the menu.

The output of these menus is very simple: it’s just items in some nested menus, each with its own data like price, type, options, etc. But the work and consideration that has to go into building each menu is anything but simple.

It was clear that our customers needed something much better.

Designing the app

Believing that all the existing tools weren’t very good, we chose not to base the core design off of any other examples or prior work. Creator would be rethought from the ground up based on the needs and jobs of its users.

Fortunately when it came to understanding the use cases we had a few big advantages:

1. We were working closely with multiple location managers who would be end-users of Creator and would give immediate, helpful feedback;
2. All members of our support team were also users who had a very good idea of what was needed;
3. Even more conveniently, I had the experience myself of having to design order menus and understood the major needs and pain points. It is an often unappreciated advantage for the lead engineer and designer of a product to be able to build the tool that they would want to use.

I wanted the design to look and feel like it fits in with any modern office web app, like Airtable, Figma, Notion, etc. The UI needed to be intuitive and ready to use without any training. This meant we needed to use existing design patterns and models of interaction. Common tasks should take no more than a few clicks to accomplish.

This meant using design patterns like WYSIWYG displays to mimic the end-user experience, organization via easy drag & drop, in-line editing, and multiple ways of viewing the menu depending on what your goal is.

Just like other modern apps, users should feel like they can “play” around with the functionality to learn it, without getting frustrated or fearing they’ll break something.

Because we were free from the burden of following rigid customer specs, we could add features that have thus far never been done in the industry. Like the ability to easily pull in a high-quality, royalty-free photo for an item if you don’t have one.

Feedback from customers

As soon as we released Creator we began hearing positive feedback from customers.

“This will be a game changer for [our] managers.” — Regional Food Service Director

Based on further discussions with power users, we realized there was a need for more intuitive menu scheduling. So we quickly added the ability select which item menu appeared on which day.

Creator’s ease-of-use also directly led to us winning an account, taking not only the checkout but full mobile ordering infrastructure from a competitor. The competing CMS was complicated and would sometimes take many days to make changes.

For large enterprise customers, one good product or service isn’t enough. You have to get the entire ecosystem of services right, along with an amazing support team to back them up.

Creator — and other tools like it — provide a full suite for clients to use. This makes us much more sticky and also makes it easier for internal customer buy-in.

FutureBlind is devoted to covering four general topics: business, investing, tech, and design. It was initially launched in 2007. Here’s some examples of areas I’ll cover:

• Technology — in particular frontier tech like AI, Space, Bioengineering, etc.
• Progress — how we can make faster progress, in particular how we can tell better stories that drive progress
• Business & Investing — mental models about startups, investing, and business analysis
• Design — how to design better experiences and how new tools drive better design

Below are some featured posts to get started with. If you like any of them, please subscribe to the newsletter — it goes out every 2-3 months. I’m hopeful you’ll learn something and come away more optimistic.

Read more about the blog and my beliefs…

Join 400+ other friends who receive the newsletter:

Subscribers will get a roundup of posts and other things I find interesting about every quarter. I’ll also do the occasional post as a podcast episode.

Featured posts to get started with:

• Advantage Flywheels — Competitive advantage can be represented visually as 1 or more feedback loops.
• Take the Iterative Path — How SpaceX moves fast and blows things up to become an industry leader.
• Let’s jumpstart the new industrial revolution — What could a new industrial revolution bring? 
• Mashgin: The Future of Computer Vision — Our vision at Mashgin as of 2016.
• The Future of Space, Part I: The Setup — Why the space industry is about to take off.
• Generalists vs. Specialists (and the Specialist’s Dilemma) — How we can learn from generalist and specialist species.
• Managing Modes of Effort — A framework for managing progress at every level of abstraction.
• Product Strategy: Falcon 9 — A short product case study on SpaceX’s Falcon 9.
• The McDonald’s Success Story — The early history of McDonalds and what made them successful.
• The Restaurant Investor — A long-form essay on the turnaround of Stake-n-Shake and what it takes to succeed in the chain restaurant business.
• The Progression of Innovation — Know where a company fits in the progression of innovation.

Interviewing is actually not very helpful. Or at the very least it is extremely difficult to judge how someone will perform with just interviews, especially when unstructured. (See here, or all the data Google collected.)

Why?

• People interviewing usually have relatively little experience, and thus have a poor “base rate” to judge the candidate against.
• Answers to questions generally have very little correlation with actual performance.
• It’s difficult to extract enough information (even in long interview processes) to make a proper call. Imagine going on a 3-hour date, thinking it over for a few days, then asking the person to get married.

So what are other ways to know if someone will be good?

• You’re friends with them or have worked with them already.
• Pointed reference checks from trusted people.
• “Trial” side project or task requiring interaction with team. Getting as close as possible to a real working environment.
• Recruiter who is both (1) very familiar with your needs/culture, and (2) specialized in hiring for that role.

But short of these things interviews are still necessary. Regardless of the specific process, it is important to have a set plan and follow it for every candidate.

Some advice:

• Prepare: don’t go into an interview cold. Know what you want to get out of them and have a clear plan for how to evaluate them.
• Let them do the talking. You should only guide them and push them. Ask follow ups: Why? What did you do about it? How come?
• Brain teasers don’t work, and aren’t indicative of anything.
• The most effective questions are situational rather than just having them recall the past. “Instead of asking candidates to describe how they handled a unique situation in a previous job or organization, it’s more fruitful to describe consistent situations that candidates could face in this job or organization, and ask them what they would do — or how they would reason.“
• Encourage them to ask questions — about your questions, you, or the company.
• Be transparent and open about your entire hiring process.
• Get away from your desk or room: Take them out, take a tour of offices, etc.

The main things you’re trying to get are:

• Excitement test. Would hiring this person make you more excited to work at the company?
• What can they do now, and how quickly could they be productive?
• How is this person going to be performing in 1 year from now?
  • How long does it take for them to learn something new?
  • What’s their growth mindset and can they continually get better?
• Will they work well with the team?
• How long are they willing to keep pushing on a good project until giving up?
• How hard is it for them to change their mind or adjust course?
• Do they do the right thing even when they don’t have to?

Aside from specific skills, what traits are the best indicators of these?

• Integrity: not just honesty, but integrity with themselves, their ideas, and “doing the right thing” when necessary. They seek out truth and embrace failure.
• Social intelligence: works well with others and is empathetic/caring.
• Intelligence
  • Raw intelligence
  • Creativity in problem solving
  • Adaptability
  • [One of the best ways I found to test for this is to ask about something they really enjoyed working on. Then grill them with questions about it, diving as deep as possible into the details.]
• Drive: is self motivated and can push themselves to get things done, even if it’s not enjoyable work (grit). More internally motivated than externally.
• [Curiosity: This could be part of intelligence or drive, but it needs to be tested for somehow. I liked to ask questions like “What things are interested in outside of work?” or even better “Pick a topic that’s not part of your day job (hobby, book, subject) and take a few minutes to explain it.”]

Warren Buffett: “In looking for someone to hire, you look for three qualities: integrity, intelligence and energy. Without the first the other two will kill you.“

There’s also a problem with hiring the “best” — they are either extremely expensive or have unlimited options so will want to work elsewhere. This is like the Moneyball problem in baseball: the best teams will have the best reputation and most resources to get the best players. But these players aren’t necessarily the only best — they are just the ones who look really good based on the most obvious metrics.

So what do you do? Look for talent in places with low competition, that require more work, or who are too “different”:

• Growth potential — people who are young or with little experience in the area, but are smart, driven, and internally motivated. You want people at the start of their “performance curve” — in the 80th percentile that can move up to the 97th over time. [I think I’d change these numbers now. Finding someone in 80th percentile is too low. If inexperienced, you still want them in 90th with ability to move to 99th.]
• Interest — people with unusually strong interest in your product or mission.
• Small fish in a big pond — picked-over, under-utilized talent in large companies who can thrive on a smaller team. [You have to be careful here. Many people, although talented, can work under the bureaucracy of a big company for years and it drains them of the ability to get things done fast.]
• Different — too outside the traditional track to be easily seen or picked up by others.

What about at the team level? What’s the right mix of people?

• Diversity of thought and backgrounds is very important. You want people with good traits (good character, drive, etc.) and driven toward the same goal(s) but with a wide variety of experiences/backgrounds, and hence ways to think about problems. You don’t want to hire a bunch of clones — that may work short term for some problems but will break when things change. See here for facts about workplace diversity in general. [Addendum: this is less important at the very beginning (seed) stage of a startup. With only a handful of people you may want similar types to get along better.]

Good materials:

• https://medium.com/evergreen-business-weekly/why-interviewing-prospective-hires-is-futile-and-how-to-make-it-only-slightly-less-futile-5d1d493ef5bc
• http://firstround.com/review/The-anatomy-of-the-perfect-technical-interview-from-a-former-Amazon-VP/
• https://www.nytimes.com/guides/business/how-to-hire-the-right-person

Stripe is a good case study of hiring processes:

• Jobs page
• On-site interviews for Engineering: What to expect (PDF) [Link is now broken — if anyone still has access to this let me know.]
• What is the engineering interview process like at Stripe?
• How Stripe built one of Silicon Valley’s best engineering teams [Greg Brockman (now president at OpenAI) talks about how Stripe built out their engineering department.]

This month marks the 15th anniversary of my first post on the FutureBlind blog. This is such a long time in the internet age that I feel like an old man now. I started the blog in college as a place for my thoughts on investing and business case studies. What I’ve written about over the years has morphed along with my interests, and I continue to enjoy putting my thoughts out there. I’ll keep going as long as I’m able to and hope readers continue to find it enjoyable! 

In this roundup edition:

• Essay: Take the Iterative Path — How SpaceX innovates by moving fast and blowing things up.
•  The AI art renaissance — What kinds of crazy applications will the AI art models lead to?
• Energy! — Energy superabundance and Mark Nelson on nuclear.
•  Space updates — Will we see SLS and Starship launch the same month?
•  What negatives does technology cause? — How do we distinguish potential risks of new tech?
•  Company Spotlights — Rocket Lab and Perimeter Solutions.
•  Interesting Links — All about Polaroid, why American can’t build, and the little ways the world works.
•  Book notes — How Innovation Works, Where Good Ideas Come From

Take the Iterative Path

This essay is also available as an episode on the FutureBlind podcast. Check it out below or subscribe to the feed in: Apple, Overcast, Spotify, Pocket Casts.

One of the greatest business successes over the last 20 years has been SpaceX’s rise to dominance. SpaceX now launches more rockets to orbit than any other company (or nation) in the world. They seem to move fast on every level, out executing and out innovating everyone in the industry.

Their story has been rightfully told as one of engineering brilliance and determination.

But at its core, the key their success is much simpler.

There’s a clue in this NASA report on the Commercial Crew Program:

SpaceX and Boeing have very different philosophies in terms of how they develop hardware. SpaceX focuses on rapidly iterating through a build-test-learn approach that drives modifications toward design maturity. Boeing utilizes a well-established systems engineering methodology targeted at an initial investment in engineering studies and analysis to mature the system design prior to building and testing the hardware. Each approach has advantages and disadvantages.

This is the heart of why SpaceX won. They take an iterative path.

Continue reading “Take the Iterative Path” . . .

Other thoughts

 The AI art renaissance

Unless you haven’t been on the internet in the past 6 months, you’ve already heard about AI art generators like DALL-E 2, Midjourney, Stable Diffusion, and Google’s Imagen.

I can’t begin to imagine all the crazy applications of this there will be in the coming years (and months!). As usual, there will be bad use cases (see the section “What negatives does technology cause?” below on this). But AI art is a particularly fun one to think of all the amazing positive use cases.

Custom, more specialized versions of models will be common and we’re already seeing them adapted from the SD model. One of the biggest benefits of neural nets in general is that you can build off of existing models with minimal retraining: you are essentially using its prior understanding of how to translate text prompts into images, and layering on further knowledge. The new model needs to get trained again but only really the last few “layers” — the computationally expensive part of the model is already good to go.

A few examples:

• Create a model trained specifically on comic books. Plug in a storyline, output the comic, and the storyteller chooses amongst different styles, etc. Here’s an example of someone that already used Midjourney to design a comic strip.
• DreamBooth, which originally used Imagen but has now be ported to SD. This solves the problem of models not being able to integrate untrained data. You can put in photos of yourself and put them in the generations. This is an obvious use case that will take off quickly.I can imagine in a few years having it productized by Apple or Google. Right now you can ask Google Photos to find photos of you on the beach, and it finds them. With this feature you’ll be able to search for “me on the top of Everest” and get back a photo of you triumphantly waving a flag on the summit of Mt. Everest that no human on Instagram will be able to distinguish from reality.  Corridor Crew: “Is This the Death of VFX?” where they use DreamBooth to create a story using all the crew.

Existing large models seem to already be better at certain things. In my experiments I’ve found DALL-E to be best for the images I’m looking for (see all the illustrations in the essay above). However Midjourney’s more fantasy-like digital art is pretty astounding. Make sure to checkout their showcase below, and refresh it every week as they update it.

 Midjourney Community Showcase

Ultimately this will just become another tool in the creative toolbox. A really really powerful one. And yes, it will automate a lot of artistic work, especially of the low-level one-off job variety. It will also create a lot of opportunity for people to be more creative.

 Cleo Abram’s “The REAL fight over AI art” addressing some of the moral and ethical concerns.

Energy!

Must read: “Energy Superabundance: How Cheap, Abundant Energy Will Shape Our Future”, a paper by Austin Vernon and Eli Dourado. What are some of the effects of very low energy costs? Where will increased demand come from? They go over the details of how abundant energy could affect transportation, water, agriculture, and more.

Pair with this report on the Clean Energy Transition, and Packy McCormick’s “Working Harder and Smarter” on why we want to use more energy (and not just make things more energy efficient).

Speaking of energy abundance — We need more elemental power! Mark Nelson, managing direct of Radiant Energy Fund, has been making the rounds recently on the current state of nuclear.

 My friend Eric Jorgenson did an interview where he gave a great rundown on nuclear’s history and where it stands today.

 Liberty also did 2 long interviews with Mark that went in-depth on the topic. Here’s part 2.

It’s not all positive — here’s a negative nuclear article in the New Yorker. Although the author admits he’s been a critic of nuclear since the 1970s, so I don’t know how partial he could suddenly become after so much confirmation bias over the years.

 Space updates

Will we see a November launch of both NASA’s SLS and SpaceX’s full-stack Starship to orbit?

NASA’s SLS launch has been a long time coming. The building/testing has been delayed for years, and now the launch itself has been scrubbed a few times (from a combination of bad luck and questionable decisions). With hurricane Ian on the horizon, the current estimated launch time is November.

The best rundown of how we got to now with the SLS (as usual) comes from Eric Berger:

With the launch just days away, I am incredibly happy for the people at NASA and the space companies that have worked hard, cut through the bureaucracy, managed thousands of requirements, and actually got this rocket built. And I’m eager to see it fly. Who doesn’t want to watch a huge, Brobdingnagian rocket consume millions of kilograms of fuel and break the surly bonds of Earth’s gravity?

On the less happy side, it remains difficult to celebrate a rocket that, in many ways, is responsible for a lost decade of US space exploration. The financial costs of the program have been enormous. Between the rocket, its ground systems, and the Orion spacecraft launching on top of the stack, NASA has spent tens of billions of dollars. But I would argue that the opportunity costs are higher. For a decade, Congress pushed NASA’s exploration focus toward an Apollo-like program, with a massive launch vehicle that is utterly expended, using 1970s technology in its engines, tanks, and boosters.

Effectively, NASA was told to look backward when this country’s vibrant commercial space industry was ready to push toward sustainable spaceflight by building big rockets and landing them—or storing propellant in space or building reusable tugs to go back and forth between the Earth and Moon. It’s as if Congress told NASA to keep printing newspapers in a world with broadband Internet.

Elon is also saying that November is likely for an orbital Starship launch. Yes, this is Elon-time, but one can hope, right?

 What negatives does technology cause?

How can you push forward at a fast pace and still get ahead of unintended consequences?

Jason Crawford writes more about this in “Towards a philosophy of safety”.

I followed up in this forum post asking how we can classify the negative effects of technologies in order to better prevent them.

Kevin Kelly wrote a good post putting the effects into 2 classes: “Class 1 problems are due to it not working perfectly. Class 2 problems are due to it working perfectly.”

It’s clear that separating different types of potential risk is important. For example: whether or not a problem has fat tail, extremistan-world consequences, and how difficult they are to control. Novel pathogens come to mind here.

 Company Spotlights

Rocket Lab ($RKLB) is the only other commercial launch provider aside from SpaceX that is regularly delivering customer payloads to orbit. They currently focus on small payloads (<300 kg) with their Electron rocket, and are planning on moving up-market with the development of their reusable Neutron rocket (13,000 kg to LEO). They’re also moving across the space stack by building satellites and many other components necessary for the space economy. One of their upcoming launches is even interplanetary: a probe to Venus, the first ever privately funded venture to another planet. (Disclosure: I own shares in Rocket Lab through the fund I manage.)

Perimeter Solutions ($PRM) has a near monopoly on the fire retardant used for wildfires. It is co-chaired by Nicholas Howley (founder of Transdigm) and William Thorndike (author of “The Outsiders”). The company has an interesting history, first starting as a division within Monsanto, spun off in 1997. Through a series of private equity acquisitions, it was renamed “Perimeter Solutions” in 2018 and continued to grow via acquisitions until going public via SPAC in 2021. Post going public, they seem to be taking the “outsider” approach and acting more as a holding co with the goal of maximizing shareholder returns.

 Interesting Links

• Stories of Our Future from World’s Fair Co. — A few months ago my friend Cam Wiese showed me a preview of artwork he commissioned from Don Clark, the same artist who did the NASA JPL posters. Now they’re finally available as posters! The vibe is amazing and I highly recommend checking them out. I’ll be buying a few myself.
• All about Polaroid : In 2018 I wrote a short post on the story of Polaroid and how Apple was their spiritual successor. Recently David Senra broke the company down on Business Breakdowns. On David’s Founder’s Podcast — all episodes of which are now freely available after being acquired by Colossus — he’s done many episodes on Ed Land and Polaroid as well. I’ve listened to them all and am glad they’re finally available to share:
  •  Polaroid: The Genius of Edwin Land (Business Breakdowns)
  •  Edwin Land: The Story of Polaroid
  •  Edwin Land: Polaroid and the Man Who Invented It
• Why America Can’t Build, by Brian Balkus. Why are large infrastructure and public-works projects so hard to build these days? Brian Balkus dives into the reasons with many great stories, finding that one of the big reasons is NEPA. Speaking of which, here is a great primer on how NEPA works from Brian Potter of Construction Physics.

These twin problems are only getting worse. President Biden has signed executive orders strengthening construction unions and increasing the stringency of NEPA requirements. Meanwhile, consulting firms are already promoting to investors the lucrative opportunities presented by the anticipated wave of retirements at public works agencies. Up to 41 percent of the construction industry’s workforce is nearing retirement age as well, and the construction industry has an estimated worker shortage of 650,000 people, partly because many who left the industry following the financial crises never returned. . . .

The result is that the U.S. gets less and less every year for every dollar spent on construction. This means decline.

• Little Ways the World Works, by Morgan Housel. Morgan does a fantastic job of simplifying concepts, and in this post describes a bunch of cross-disciplinary mental models.

 Book notes

Occasionally I’ll put out notes I’ve written on some of my favorite reads. Below are a few recent ones:

How Innovation Works, by Matt Ridley

Where Good Ideas Come From, by Stephen Johnson

One of the greatest business successes over the last 20 years has been SpaceX’s rise to dominance. SpaceX now launches more rockets to orbit than any other company (or nation) in the world. They seem to move fast on every level, out executing and out innovating everyone in the industry.

Their story has been rightfully told as one of engineering brilliance and determination.

But at its core, the key their success is much simpler.

There’s a clue in this NASA report on the Commercial Crew Program:

SpaceX and Boeing have very different philosophies in terms of how they develop hardware. SpaceX focuses on rapidly iterating through a build-test-learn approach that drives modifications toward design maturity. Boeing utilizes a well-established systems engineering methodology targeted at an initial investment in engineering studies and analysis to mature the system design prior to building and testing the hardware. Each approach has advantages and disadvantages.

This is the heart of why SpaceX won. They take an iterative path.

Taking the determinate path

Let’s talk about the Boeing philosophy first, which is the most common approach taken by other traditional aerospace companies. “There are basically two approaches to building complex systems like rockets: linear and iterative design,” Eric Berger writes in the book “Liftoff” about the early history of SpaceX:

The linear method begins with an initial goal, and moves through developing requirements to meet that goal, followed by numerous qualification tests of subsystems before assembling them into the major pieces of the rocket, such as its structures, propulsion, and avionics. With linear design, years are spent engineering a project before development begins. This is because it is difficult, time-consuming, and expensive to modify a design and requirements after beginning to build hardware.

I call this the “determinate path” — in trying to accomplish a goal, the path to get there is planned and fixed in advance.

In project management this method is called waterfall, an “approach that emphasizes a linear progression from beginning to end of a project. This methodology, often used by engineers, is front-loaded to rely on careful planning, detailed documentation, and consecutive execution.”

Spend a lot of time scoping and planning carefully upfront, then move progressively forward step-by-step. This is the “measure twice, cut once” approach.

You may be familiar with it as it’s very common in organizations everywhere.

There can be many reasons why this path would be taken:

• If from the start you have very clear, unambiguous requirements (from customer, management, etc.)
• If you think you can figure out how exactly to build something before building it, you’d probably want to plan it all in advance.
• If your fixed costs are high, it can force you to make decisions up front. Take traditional auto manufacturing. A door mold machine might cost $50 or $100M, so you have to figure out what the design of the door will be first. (But this means if later they have a new idea for a better car door, they don’t want to change it because of the sunk costs of the mold machine.)
• You have a lot of resources, which makes you think you can just brute force it and overwhelm the problem with money and people. (Many overfunded startups are guilty of this.)

But there is another way . . .

Taking the iterative path

When I think of the most impactful technologies over the last 100 years, nearly all were created by small teams of tinkerers.

Why? It’s easier for these teams to take an iterative path.

Taking this path means rapid prototyping, testing concepts against reality, failing, and adapting. Continuing from the book “Liftoff”:

The iterative approach begins with a goal and almost immediately leaps into concept designs, bench tests, and prototypes. The mantra with this approach is build and test early, find failures, and adapt.

Focus more on building and finding failure modes than making things perfect. Project managers call it “agile”, or at Facebook, “move fast and break things.”

The canonical example of this to me is the Wright brothers, previously bicycle mechanics, building iterations of their airplane design over and over, and failing until they succeeded.

This approach ended up being common in the origin stories of all airplane manufacturers and defense companies — Martin Marietta, Lockheed, Northrop Grumman, etc., where again you had relatively small teams of self-taught tinkerers building complex machines through a process of iteration, failure, and learning until they succeed.

How can you reconcile this “fail fast” approach with the care that’s needed to reliably build things where human lives are on the line?

The answer is that these can be two different parts of the organization. Working together, but with different focuses. “[SpaceX is] launching 5 or 6 times a month and on their pads they need operational excellence with zero risk — you know, they’re doing innovation but it’s minimal innovation. Blowing things up on the pad is not a good idea — you want that down to zero because human lives and certainly lots of capital is at risk.” This is Steve Blank on a recent Village Global podcast. He continues:

But on the other hand, they have another part of the company that in fact believes in not only blowing things up on the test pad — because if you’re not doing that you’re not pushing the envelope fast enough — it’s the cycle time of doing that. So they have an agile innovation process.

Now think about that. This is the same company doing two very different things with two different groups of people, two different risk profiles, but more importantly they’re talking to each other. It’s not “here are the smart people, and here are the people turning the crank,” they’re learning from each other. The guys building the raptor engines and Starship need to know where the GFC plugs in and what the right materials and things they need to get right on the next rocket. And the people doing the existing rockets can learn about new materials and incremental upgrades so they are innovating but innovating with minimal risk.

The iterative path is easier to take when you’re nimble and the cost of failure is low. This is why it’s so common in software. But as the previously mentioned companies have shown, it’s also the best approach in hardware and complex, frontier tech.

And just as the traditional aerospace companies have demonstrated, organizations that are very bureaucratic now were almost always more iterative in the past.

The early history of Lockheed’s Skunk Works division is informative, which I believe later served as one of the models for SpaceX’s approach. Skunk Works was an R&D group created by Kelly Johnson within Lockheed during the war in 1943 when they got the contract to build the P-80 Shooting Star. From a documentary on the birth of Skunk Works:

Lockheed was already swamped in terms of manpower, tooling, and facilities with wartime contracts but this was a blessing in disguise, an opportunity to implement an idea he’d been pestering Robert Gross about for years. Let him round up a small group of talented people: designers, engineers and shop men. Put them under one roof where they could all work closely together and give him complete authority over everything from procurement to flight tests.

Johnson gathered 28 engineers including himself, and 105 “shop men” (I assume this just means workers who can build what the engineers design) and built a small facility out of discarded shipping crates using a circus tent for a roof. He then laid out the original rules that would become the foundation for Skunk Works over the next 30 years:

. . . he’d be responsible for all decisions. Paperwork and red tape would be cut to the minimum. Each engineer would be designer, shop contact, parts chaser, and mechanic, and each would remain within a stone’s throw of the shop at all times. . . . Forcefully reminded that simplicity is the keynote of good design, the designers jumped into their work. But this was a new kind of operation, and instead of moving from stage to stage, the schedule demanded an extraordinary degree of concurrency.

The time from initial concept to delivery of the first P-80 to test pilots would be only 5 months. In fact, nearly all of the early planes coming out of Lockheed took less than 6 months — less than 6 months from concept to delivery. Crazy!

Even the famous A-12 (later the SR-71 Blackbird) look less than 4 years from initial idea to roll out. This may seem like a lot when you’re used to super-fast software timelines, but this is 4 years for one of the fastest, most successful aircraft ever built.

The scrappy culture lived on in later Skunk Works projects. This is Ben Rich, who led the division in later years, on their building of the F-117 (this is the Darth-Vader-looking stealth fighter you’ve probably seen before):

On the F-117, we had to get the guy to climb into the cockpit. So I went to the local builders mart, and bought one of these ladders for 50 bucks, and we just used it. . . . We didn’t have to spend thousands of dollars designing it for Mil spec — military specification — and we did simple things like that.

The more you learn about the history of building things, the more you hear stories like this, even with highly complex innovations. The development of the Sidewinder missile is another interesting example: again, small team, rapid iteration, creative solutions to problems.

Why is iteration better?

Taking the iterative path tests your model against reality, getting to the truth as fast as possible.

There are a few major downsides to the linear approach:

1. Clear specs and requirements from the outset may seem like a good thing. Much of the time though they don’t match reality though. This is especially true in areas that are pushing the boundaries of innovation.

   Over time, “the spec” becomes the most important thing. Here’s Ben Rich again, on one of the requirements for building the SR-71 Blackbird:

   Some general insisted that there was a military spec that the SR-71 had to say ‘U.S. Airforce’ and the stars and bars. I said ‘General . . . you’re crazy.’ I said, you know, this has the temperature of an oven. Have you ever taken a piece of metal, painted it and stick it under your broiler? You can’t keep the paint on the metal. He said ‘No, the spec says you gotta say U.S. Air Force on our airplanes.’ I said we’ll develop it. So we spent a million dollars developing a paint that could show red, white and blue, and we put it on the airplane. . . . I mean, who’s going to see you at 90,000 feet?

   In this example and many others, politicians start dictating how work should be done, rather than just setting the goal like they should be. Conditions for funding become completely removed from the outcome itself, like mandates to use certain suppliers or base employees in certain states.

2. The technical scope is too large, so that when there’s a problem, it’s hard to find the root cause. When there’s a problem you have to go back to the drawing board, but you may not even be able to do that given the cost to start over.

3. You become too risk averse, fearing failure. This is pretty simple: if the costs to start something or fail are high, people don’t want to do new things. From the book “Liftoff”:

   At most other aerospace companies, no employee wanted to make a mistake, lest it reflect badly on an annual performance review. Musk, by contrast, urged his team to move fast, build things, and break things.

   And from an executive of Blue Origin on what they do wrong:

   I believe we study a little too much and do too little . . . More test [rather than] more analysis will allow us to progress more quickly, iterate, and eventually succeed.

A good iterative approach creates tight feedback loops, like John Boyd’s classic OODA loop: observe, orient, decide, act. Observe what’s going on, orient yourself to the environment, decide what needs to be done to make progress, act on that decision, and return to observing the results from your action. From the book “Certain to Win” on Boyd’s philosophy:

What does it take to win? Agility — the ability to rapidly change one’s orientation (worldview) in response to what’s happening in the external world.

This was referring to combat. But it’s just as true in business and engineering.

Tight feedback loops lead to a high rate of innovation and adaptation, quickly finding better solutions and what not to do. Speed is a tactical advantage.

“Innovation per year is what matters. Not innovation absent time. . . . What is your rate of innovation? that matters. And is the rate of innovation, is that accelerating or decelerating?

https://www.youtube.com/watch?v=ReXzsnyDq_M

Elon Musk

Brian Armstrong, founder of Coinbase, has a good saying that “action produces information”. You can’t predict the future, so just start building. And that’s what SpaceX did. Eric Berger writes that “the engineers designing the Falcon 1 rocket spent much of their time on the factory floor, testing ideas, rather than debating them. Talk less, do more.”

The iterative path in practice

So I’ve convinced you that iteration is best. What does it mean in practice?

Agile is straightforward for software, but not so much hardware.

Historically, linear/waterfall has been easier to do in hardware, especially for large engineering projects. A lot of upfront costs are needed — so spend a lot of time gathering requirements and planning before actually building.

For companies building big, complex, expensive things, it seems like a reasonable assumption that you have to know exactly what you’re doing, plan a lot, and be risk averse. As you know, this isn’t true! You can be fast, nimble, and agile even in megaprojects.

In complex hardware what this means is:

1. Being hardware rich — having lots of spare parts and backups.

   This lets you move quickly, and continue to try things over and over, because you have all these “at bats”. I’d include 3D printing as it allows you to create parts on an ad hoc basis.

2. Using simulations — move atoms to bits when possible, giving you the freedom to quickly test and have all the benefits of software. If you can simulate what’s happening in the real world with enough accuracy, you can fail as much as you want. This is another area that has changed a lot in past decade or so. Here is Cliff Berg on SpaceX’s use of simulation:

   SpaceX has invested a great deal of effort in automating the process of designing and validating through simulation, and delivering the machinery that they build through automation. They use traditional computer-aided design (CAD) tools such as CATIA, but they also invested in an end-to-end 3D modeling system from which they can view and simulate entire assemblies and automatically print parts. Importantly, the software is fast, even when handling complex assemblies, so that engineers do not have to wait, which encourages a rapid iterative design approach.

   And further from a slide in a presentation given by SpaceX Director of Research Adam Lichtl and Lead Software Engineer Stephen Jones:

   Why Simulation?
   1. Investigate what cannot be measured
   2. Reduce need for testing
   3. Design optimisation: narrow design space
   4. Proactive instead of reationary design

3. Constantly testing the whole system. Simulation is great but in the end you have to actually test everything out.

   3. Test at the full, integrated system level (in flight ideally) as quickly as possible. Paper analyses and simulations only take you so far and break down quickly at interfaces between complex subsystems.

   — Skyler Shuford (@skyshuford) February 19, 2022

   In these complex systems, in the end what really matters is how the whole thing behaves in the real world. This reminded me of how George Mueller, who led NASA’s spaceflight program during the Apollo missions in the 60s, approached building the Saturn rocket:

   At a system level you’re much better off testing the system [rather than the individual parts] because in the end that system has to work. And then the only way you find out is if you test it as a system.

   Subsystems of the rocket would only be tested if needed.

4. Utilizing “pathfinders”. In manufacturing, a pathfinder is an early build of something that won’t end up seeing the light of day.

   You build a pathfinder to see where problems are, and how it can be done better. You know it will fail or be suboptimal, you’re just looking for how to do it better.

   This is similar to a “tracer bullet”. You fire it first with no expectation of hitting the target, watch it, and then adjust your aim.

Doing all of these things, like SpaceX, leads to much faster iteration. This is something many other companies can learn from.

In summary: When a goal is big and complicated, an iterative, fail-fast approach is much better than a linear approach. Determinate paths lead to slower, poorly-adapted solutions, whereas iteration finds problems faster with a result that’s better adapted to the real world.

Thanks to Rohit for providing feedback on the draft. All illustrations in this essay are a combination of DALL-E generations and my personal edits.

ELI5: Innovation is creating something new that is useful. It is different from invention, which is creating something new that is not necessarily useful. Innovation often happens by accident, and it is always a team effort. It is usually a gradual process that happens over time through trial and error. There can be a lot of resistance to innovation, because people are sometimes afraid of change. The main ingredient in the secret sauce that leads to innovation is freedom.

Innovation is gradual

Eureka moments are rare, possibly non-existent. Man-made technologies evolve from previous tech, and are not invented from scratch. This is a key characteristic of evolutionary systems: the move to the “adjacent possible” step.

If innovation is a gradual, evolutionary process, why is it so often described in terms of revolutions, heroic breakthroughs and sudden enlightenment? Two answers: human nature and the intellectual property system. Very few people have much incentive to argue that invention is gradual.

Innovation is different from invention

Tim Harford: “The most influential technologies are often humble and cheap. Mere affordability often counts for more than the beguiling complexity of an organic robot.”

Fritz Haber’s discovery of how to fix nitrogen was a great innovation. But it was Carl Bosch’s years of hard experiment, overcoming problem after problem and borrowing novel ideas from other industries that eventually led to large scale and a price that society could afford to pay.

Again and again in the history of innovation, it is the people who find ways to drive down the costs and simplify the product who make the biggest difference.

Joseph Shumpeter: “The capitalist achievement does not typically consist in providing more silk stockings for queens but in bringing them within the reach of factory girls in return for steadily decreasing amounts of effort.”

Innovation is often serendipitous

It is a well known attribute of innovation: accidental discovery.

Innovation is recombinant

Every technology is a combination of other technologies; every idea a combination of other ideas. “Novel technologies arise by combination of existing technologies and that (therefore) existing technologies beget further technologies.”

Innovation happens when ideas have sex. It occurs where people meet an exchange goods, services and thoughts.

In biology, little mistakes (point mutations) are the fuel of evolution. But Andreas Wagner argues such small steps cannot help organisms cross valleys of disadvantage to find new peaks of advantage. Sudden shifts of whole chunks of DNA, through crossing over, or through so-called mobile genetic elements, are necessary to allow organisms to leap across these valleys. The extreme case is hybridization. Wagner: “Recombination is much more likely to preserve life — up to a thousand times more likely — than random mutation is.” Bacteria can “catapult themselves not just hundreds of miles, but thousands of miles, through a vast genetic landscape, all courtesy of gene transfer.”

Innovation involves trial and error

Most inventors find that they need to keep “just trying” things. Tolerance of error is therefore critical. Playfulness probably helps too. Innovators who just like playing around are more likely to find something unexpected.

Innovation is a team sport

It is always a collaborative phenomenon. This pattern is the rule, not the exception, and it was the flowering of societies, clubs and mechanics’ institutes that gave Britain its lead in the industrial revolution.

Innovation is inexorable

People seem to stumble on the same idea at the same time. Technology is absurdly predictable in retrospect, wholly unpredictable in prospect.

Innovation’s hype cycle

People tend to overestimate the impact of a new technology in the short run, but to underestimate it in the long run.

Innovation prefers fragmented governance

Empires are bad at innovation. As time goes by and the central power ossifies, tech tends to stagnate, elites tend to resist novelty and funds get diverted into luxury, war or corruption.

In the history of China, periods of explosive innovation coincided with decentralized government.

This fragmentation works best when it results in the creation of city states.

Innovation is the mother of science as often as it is the daughter

It is just as often the case that invention is the parent of science: techniques and processes are developed that work, but the understanding of them comes later. Steam engines led to the understanding of thermodynamics, not the other way round. Powered flight preceded almost all aerodynamics.

Innovation does not create unemployment

The idea that innovation destroys jobs comes around in every generation. So far it has proved wrong. Over the past two centuries productivity in agriculture dramatically increased, but farm workers moved to cities and got jobs in manufacturing.

Big companies are bad at innovation

Innovation often comes from outsiders. This is true of individuals as well as organizations.

Big companies are too bureaucratic, have too big a vested interest in the status quo and stop paying attention to the interests, actual and potential, of their customers. Many times only competition can make big companies more innovative.

There can be a lot of resistance to innovation

Innovation is the source of prosperity and yet it is often unpopular.

Coffee shops have been popular places to meet for at least 4 centuries. But coffee has had a long history of bans and prohibitions.

Here we see all the characteristic features of opposition to innovation: an appeal to safety; a degree of self-interest among vested interests; and a paranoia among the powerful. Recent debates about GMO food or social media echo these old coffee wars.

Even intellectual property can stifle innovation.

For example there is no evidence from geography and history that patens are helpful, let alone necessary, in encouraging innovation.

Patents undoubtedly raise the cost of goods. The justification of the patent system is that by slowing down the diffusion of technical progress it ensures that there will be more progress to diffuse. But this does not necessarily happen.

Finally, patents tend to favor inventions rather than innovations: upstream discoveries of principles, rather than downstream adaptation of devices to the market.

Innovation is one of those things that everybody favors in general, and everybody finds a reason to be against in particular cases. Far from being welcomed and encouraged, innovators have to struggle against the vested interest of incumbents, the cautious conservatism of human psychology, the profitability of protest, and the barriers to entry erected by patents, regulations, standards and licenses.

How innovation works

The main ingredient in the secret sauce that leads to innovation is freedom. Freedom to exchange, experiment, imagine, invest and fail; freedom from expropriation or restriction by chiefs, priests and thieves; freedom on the part of consumers to reward the innovations they like and reject the ones they do not.

This reliance on freedom explains why innovation cannot easily be planned, because neither human wishes nor the means of their satisfaction are easy to anticipate in the detail required.

In this roundup edition:

•  To Increase Progress, Change Culture: Why progress needs better marketing.
•  We need a new World’s Fair
•  Company (Startup) Spotlights: Hadrian, First Resonance, and Mashgin.
•  Request for Podcast Series
•  Interesting Links: “The man in the arena”, Grid scale energy storage, Kevin Kelly’s advice, the metaverse, jobs-to-be-done for investing, and how companies die.

 To Increase Progress, Change Culture

The key to faster progress is increased desire for more. That’s my theory, at least.

In all the commentary on the “Great Stagnation”, much is written about the lack of progress in tech areas like transportation. Commercial airplane speeds, for example, have decreased on average since the ‘70s:

Since 1973, airplane manufacturers have innovated on margins other than speed, and as a result, commercial flight is safer and cheaper than it was 40 years ago. But commercial flight isn’t any faster—in fact, today’s flights travel at less than half the Concorde’s speed. (Airplane Speeds Have Stagnated for 40 Years, by Eli Dourado and Michael Kotrous.)

There are clearly many contributors to this. Regulation is cited in the above post and seems to be most common reason mentioned. Rising energy costs is another major one. The less-talked-about contributor is consumer demand.

Continue reading…

 We need a new World’s Fair

Speaking of marketing progress, I talked more about why we need a new World’s Fair in this thread:

 Company (Startup) Spotlights

Hadrian is building the factory of the future — highly automated, software-first, and at huge scale. They’re starting upmarket by building high-precision parts for aerospace hardware. A lot of current space components are made in small machine shops. This worked for the Apollo and Shuttle eras but won’t cut it if the US wants to win Space Race v2. Hadrian has contracts with large space companies and plans to expand into other metals, industries, and types of manufacturing. I referenced Hadrian in a past thread on programmatic atoms here. Even if you’re not interested in manufacturing, space, or automation (anyone?) I would highly recommend reading or listing more about Hadrian — Not Boring: full rundown of their vision;  Josh Wolfe & Chris Power on Invest Like the Best.

First Resonance is building an operating system for factories. This is another entry in one of my favorite categories of “software for building hardware”. Manufacturing in general has surprisingly low usage of modern software stacks. I started to enter this area myself (manufacturing automation) in 2017 and from everything I saw, very little was automated and not much data was collected. Some very specific verticals were more advanced but a lot of it was living in the ‘80s. Having an easy-to-use (and set up), centralized spot to track workflows and performance would be huge.  Interview with cofounder Karan Talati.

Mashgin has built a self-checkout kiosk that uses computer vision to recognize items so you don’t have to scan barcodes. This spotlight is a bit of self-promotion, as I’m an investor and early employee in Mashgin going back to 2015. Mashgin recently announced we were profitable and have raised a $62M Series B from NEA, Matrix and a few customers. The checkout kiosk is built for small-format locations like convenience stores, cafeterias, and sports venues. Earlier this month Couche-Tard announced they would be rolling out 10,000 to CircleK C-stores around the the world. Mashgin is the largest “smart checkout” Point of Sale in the world and this deal cements it as the leader. Forbes: Mashgin Hits $1.5B Valuation With AI-Powered Self-Checkout System. Below is a longer thread I wrote of my thoughts on the future of physical checkout:

 Request for Podcast Series

Check out Patrick Collison’s Fast entries if you haven’t already. I would love to listen to this, so… if someone out there is itching to produce a new podcast then please steal this idea.

 Interesting Links

• Teddy Roosevelt’s full speech “Citizenship in a Republic”. This is the “man in the arena” speech that everyone has read and heard bits and pieces of, but like me, never read the whole thing. A truly amazing speech from start to finish and one that I’ll try to revisit from time to time. And a good reminder that it’s possible to have great people leading the country. H/t Patrick O’Shaughnessy
• Grid Scale Energy Storage Deployment by Technology Part 1, Part 2. Sarah Constantin explains and breaks down large scale energy storage solutions. If we want to deploy a massive amount of solar and wind, we’ll need ways to store a huge amount of energy efficiently. Sarah goes over existing and potential solutions to this. Super interesting — I had never given this much thought but it will be an area ripe for innovation and profits.
• Kevin Kelly: 10 Bits of Advice I Wish I Had Known. Another amazing list of advice, his 3rd in the series, from Kevin Kelly. “Whenever there is an argument between two sides, find the third side.” “Don’t wait for the storm to pass; dance in the rain.” “The chief prevention against getting old is to remain astonished.”
• Why the metaverse needs crypto. Good thread defining the metaverse (yeah the term is way overused now but the concept is useful) and why in the end it needs crypto/NFTs. Yes, there are use cases for NFTs despite the bubble deflation, and this goes over one of them.
• Using jobs to be done theory in investing. The author Andrew Glaser uses JTBD theory to analyze Lemonade, the online-native insurance company. JTBD analysis and interviews can be very helpful in product development and strategy so I don’t see why it also wouldn’t be in investment due diligence (“scuttlebutt”). Via Ryan Singer.
• Patterns of behavior that kill companies. A great thread with detailed systems diagrams of scenarios that lead to killing companies. Especially helpful for startups to see what interventions can be used to avoid theses fates as the company grows. Via James Rosen-Birch.

In all the commentary on the “Great Stagnation”, much is written about the lack of progress in tech areas like transportation. Commercial airplane speeds, for example, have decreased on average since the ‘70s:

Since 1973, airplane manufacturers have innovated on margins other than speed, and as a result, commercial flight is safer and cheaper than it was 40 years ago. But commercial flight isn’t any faster—in fact, today’s flights travel at less than half the Concorde’s speed. (Airplane Speeds Have Stagnated for 40 Years, by Eli Dourado and Michael Kotrous.)

There are clearly many contributors to this. Regulation is cited in the above post and seems to be most common reason mentioned. Rising energy costs is another major one. The less-talked-about contributor is consumer demand.

Most things are “good enough”

Clayton Christensen’s theory on disruptive innovation shows that as average performance demanded goes up, the performance level supplied by products generally goes up faster, eventually surpassing the majority of the market.

As a technology improves, its performance surpasses most market demand, and things became “good enough” over time. Customers aren’t willing to pay more for better performance. This leaves the market open for disruptors — either on the low-end (good enough performance but cheaper), or by having better performance on a completely different metric.

Back to airline travel. Flying from NYC to LAX in 6 hours became good enough for most people. Sure, less would be better, but not at much more cost. Only high end, richer users truly needed more. So airplane makers moved on to other attributes that weren’t good enough: safety, flexibility, price.

This was true for a lot of tech. Basically the market for that level of performance wasn’t there.

So this was less of a tech problem or lack of ability, more a lack of desire. There is diminishing marginal utility for faster travel times. Cross-country travel in 4 hours instead of 6 doesn’t make a whole lot of difference to most people. (Throw in other issues like car traffic and TSA and the minimum door-to-door travel time is 2-3 hours no matter where you’re going, so a few extra hours over 3,000 miles isn’t a huge deal.)

As Tyler Cowen writes in Average is Over, consumers are more interested in convenience than speed. We don’t want to spend $20,000 on Concorde tickets just to save a couple hours.

Of course, everything moves in cycles and it’s possible that now, after 40 years, needs like safety and prices are oversupplied and speed is finally in demand again.

The U.S. has been pretty good at having a lot of desire for more. We’re a nation of immigrants, a nation of people who are always looking for a better life. That desire has helped us create a lot of new things.

But it’s hard to maintain that level of desire. As societies get richer, they tend to get more complacent. They don’t want to rock the boat. They don’t want to take risks.

We need to increase desire

When consumer demand shifts, capitalism is there to fill in the gap. I believe this is true even despite heavy regulation. If a majority of the population wants something, regulation can be overcome.

If we want more progress, we need more desire for it. If people want more, technology will deliver more.

How do we get more desire?

This is the tricky part. Increasing the demand of a majority of people is . . . hard.

Most entrepreneurial efforts don’t increase demand — they ride on the wave of existing needs, trends, or market arbitrages. Awareness and concern for climate change is one example of a trend that has increased demand over a wide range of technologies.

Some individual companies have pulled it off — Apple and Tesla are the first that come to mind for me. It feels like they truly pulled consumer demand forward much more than it would have been otherwise. ¹

Progress needs better marketing

Increasing desire is well understood in the world of marketing and sales. But it’s also a key driver of innovation.

Marketing is manufactured desire; engineered discontent.

But “marketing” doesn’t just mean viral videos, making YouTube ads, buying Google AdWords, or promoting tweets. These usually only drive short-term desire and nudge someone to buy something they otherwise might not have. Even the most “friendly” marketing is permission-based, like Google AdWords, where the consumer is likely looking for a solution already and they are presented it at just the right time.

Larger, longer-term demand shifts are needed to truly push progress forward.

What does this entail? Some ideas:

• More inspiration: Optimistic sci-fi visions of the future (or even the present). Sci-fi has a good track record of pushing (particularly young) people’s expectations up for what amazing things the future can bring.
• Futurism: A more concrete vision of the future. See Eli Dourado’s “Why progress needs futurism” post.
• Education: Yeah, sure, all marketers are liars. But we can still tell the truth to raise expectations. Help people understand what is possible and why we need it. Make it easier for people to see the potential for more. Show them what is possible with current or future technology.
  • Things can be so much better! For you, your family, your friends, and all those less fortunate in the world.
  • The world isn’t zero-sum. We can have our cake and eat it too!
  • We’ve done amazing things in the past, and we can do them again!
• What’s in it for me? People are selfish. If it’s not clear how something can directly benefit them personally or their family and friends, why care about radical improvement?
• Progress memes: Solarpunk, Terrapunk, whatever. Even better if it’s totally exaggerated and unrealistic. It might just push the Overton window enough to drive actual change.

Footnotes

1. The De Beers diamond campaign is the best example I can think of for increasing desire on a massive scale, but this was accomplished mainly by convincing people of their rarity. It’s a little different than people wanting better performance from a product or service. Either way I highly recommend reading this article from 1982 on how De Beers did it. ︎