Tag: technology

Roundup #4: 15th Anniversary Edition

Greetings FutureBlind readers!

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
Continue reading “Roundup #4: 15th Anniversary Edition” →

Take the Iterative Path

How SpaceX innovates by moving fast and blowing things up.

Take the Iterative Path – FutureBlind Podcast

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.

Continue reading “Take the Iterative Path” →

The new wave of science and research models

There has been an increasing amount of experimentation in the philanthropic and scientific funding space over the past few years. This is good news — as I mentioned in my last post, we need better ways to fund crazy ideas.

Here’s a sampling of some of the recent efforts:

  • The Astera Institute — Pursuing new tech areas through multiple models including FROs, PARPA (based on the DARPA model).
  • Fast Grants — An effort by Tyler Cowen, Patrick Collison and others to quickly disburse grant money to COVID-related ideas. Funded by many wealthy donors and philanthropies. Impetus Grants for longevity research was recently launched and inspired by Fast Grants.
  • New Science — Funding life science labs outside of academia. Partly funded by Vitalik Buterin.
  • Arcadia Science — Bio research institute.
  • Arc Institute — Funds individuals similar to HHMI, in partnership with Stanford, Berkeley, and UCSF. Founded by Fast Grants “alumni” Silvana Konermann, Patrick Hsu, and Patrick Collison.
  • Convergent Research — Uses focused research organizations (FROs) to solve specific scientific or technological problems. Funded by Eric Schmidt’s philanthropy.
  • Altos Labs — Biotech lab, another “academia outside of academia” model.
  • VitaDAO — A DAO-based longevity funding org where holders get a cut of IP proceeds.
  • Actuate — Also using the DARPA approach to fund and implement R&D.
  • FTX Future Fund — A non-profit fund from the FTX crypto exchange, aiming to allocate at least $100M this year to a wide variety of long-term focused areas.

In “Illegible Medicis and Hunting for Outliers” Rohit observes that:

There are two common themes here. That’s speed and autonomy. They mostly act under the assumption (the correct assumption it would seem from a betting lens) that they can identify talent, not bug them excessively, and leave them to do their thing. Instead of imposing rules and strictures and guidelines, they focus on letting the innate megalomania do the work of focusing their research.

The academic and government driven funding models have come up against their limits in recent years (decades?). These experiments provide new methods to allocate capital to research, development, and implementation of efforts that for whatever reason aren’t amenable to the startup funding ecosystem.

Prior to World War II, support from non-government or educational institutions was the norm. Patrons like Alfred Loomis ran a lab at Tuxedo Park, hosting scientists and engineers from around the world that was integral in the creation of radar. Funding was provided by philanthropies from the likes of Carnegie and Rockefeller. Or private R&D from Edison, Bell Labs or Cold Springs Harbor Lab.

These past models are still doing well of course — HHMI, the Gates Foundation, Google X, etc. — but much more is needed to expand experimentation. The government can continue to play a valuable role, particularly as a buyer of first resort.

I’m super excited to see what comes from these orgs. A few like Fast Grants have already had some impact.

For more on the topic, see:

Cover photo by The National Cancer Institute, Unsplash.

Let’s jumpstart the new industrial revolution

There is as much headroom in physics and engineering for energy as there is in computation; what is stopping us is not lack of technology but lack of will and good sense. — J. Storrs Hall

There have been three industrial revolutions. The first two spanned from the late 1700s to the early 1900s and essentially created the technological world we know today. Energy, transportation, housing, and most “core” infrastructure is pretty similar now as it was at the end of this period — especially if you extend it into the 1970s. The third revolution, the “Digital Revolution”, started around this time and as anyone reading this knows has made computing and communication ubiquitous.

There were bad things that came from these revolutions: pollution, environmental destruction, war, child labor, etc. But the good overwhelmed the bad, leading to GDP per capita (”resources per person”) doing this, which we can use as a proxy for progress in a host of other areas like longer/healthier lifespan, lower child mortality, less violence, lower poverty, and more.

Wikipedia describes the potential Fourth Industrial Revolution as “…the joining of technologies like artificial intelligence, gene editing, to advanced robotics that blur the lines between the physical, digital, and biological worlds.”

These things are great, but we need more. Much more.

As just one example, it’s become abundantly clear over the past few weeks the importance of energy independence. But why don’t we already have it?

The cost of PV cells has collapsed over the past few decades. We also know it’s possible to build nuclear reactors far safer and more productive than any in the past. There should be solar panels on every home, geothermal wells in every town, and multiple nuclear fission (possibly fusion?) reactors in every state. A setup like this would lead to redundant energy at every scale, not reliant on geopolitics or over-centralization.

We should want to consume more energy, not less. (And unlike the second industrial revolution, it can be clean energy with minimal externalities.)

What else could a new industrial revolution bring? Just imagine what you’d see in a typical sci-fi movie:

Space parks/hotels/colonies, limb regeneration, flying cars, supersonic jets, same-day shipping to anywhere on Earth, self-replicating nanobots, new animal species, plants everywhere, infrastructure made out of GM trees, universal vaccines for all viruses, mobile robotic surgeons that can save lives on-location, convoys of self-driving cars, batteries with 50x current power, etc. etc.

To build these things — or even to see if they’re possible — a lot needs to change. Here’s just a few I’ve been thinking about:

  • Create a pro-progress culture. Pro-progress means anti-stasis. We’ve come a long way, and things are pretty good now. But they could be better. Far more people should be optimistic about the future and what they can do now to make it better.
  • Find more ways to celebrate and fund scientists and inventors like we do founders, celebrities, executives and sports stars. More crazy ideas should be funded, and even if they don’t succeed, the culture should be accepting of it.
  • Take more risks as a society. Incremental progress is great but even over long periods it can lead to a local optimum. To get to a higher peak, we need more exploration, experimentation, and invention. With this comes risk. We should do whatever we can to be conscious of and mitigate these risks, but in the end if the precautionary principle is applied to everything, we’ll be stuck in stasis until a global catastrophe forces our hand.
  • Allocate more resources to efforts that have high expected return to life on Earth. Nuclear fusion, for example, may have only a small probability of succeeding in the next 10 years. But if it does, it could bring enormous benefits to the world (to humans, animals, plants, you name it). The probability-weighted return to life on Earth is thus very large, and yet minimal resources are being devoted to it. The industrialization of space is another example. Concerned about depleting Earth’s resources or peak “X”? You wouldn’t be if we could mine asteroids and move potentially harmful processes off-planet.

If you agree with any of the above or are interested in similar ideas, here’s a few good resources I’ve enjoyed recently:

Singularities

The “Singularity” in artificial intelligence is the future moment when generalized AI becomes smarter than humans. In theory this starts a feedback loop of runaway intelligence that radically changes our world in ways that are hard to predict.

Similar points exist in other industries as well. These are ultra tipping points that would lead to drastic changes in the industry and our world — changes so great we could only make very rough guesses as to what they’d be.

What are some potential examples?

  • Highly reliable level 5 autonomous cars.
  • Rockets able to sustainably send a kilogram to orbit for under $100.
  • Abundant renewable energy under $20 per MWh.
  • Near perfect protein folding algorithm available via API call.
  • Low-cost ability to manufacture any protein at scale.
  • Battery cost below $100 per kWh at scale.
  • Battery energy density over 500 Watt-hours per kilogram.
  • Plant- or cell-based meat cheaper than animal meat with ~same nutritional profile.
  • Affordable VR/AR glasses with variable depth of focus and up to 60 pixels per degree of resolution (~matching the human eye).
  • A definitive method for stopping cellular senescence without noticeable side effects.
  • Cost of aerospace-grade carbon fiber comparable to aluminum. (Currently ~10x more.)
  • Cost of carbon nanotubes comparable to current carbon fiber. (Currently 5-10x more.)

Some of these tipping points look like they’re in our near future, and there’s no reason to believe any of them aren’t possible. A few of them would likely make others on the list easier. Every one of them has downsides but the upsides are massive. How exciting!

What else can be added to the above list that I forgot?

(Tipping points in brain-machine interfaces, construction and building, healthcare, etc.)

Tech Stack Trees

Every product is built on and enabled by one or more technologies.

Understanding where a product fits on its higher-level tech stack is an important part of any long-term strategy or investment thesis.

The following is an exploration of tech stacks: what they are, how to model them, and what roles their components play. I also discuss what insights can be gained, such as potential opportunities to expand the business.

Stack Trees

Typically, a tech stack shows what infrastructure a technology is directly built on or requires. A SaaS startup for example could have a front- and back-end software stack with a cloud provider like AWS under it. The tech in focus is on top of the stack, with the supporting layers below it.

A tech stack tree is a higher-level version, branching both above and below the “layer” in focus. It shows both what the technology directly relies on and what relies on it. Stacks are fractal in nature, just like trees. An innovation spawns many others that use it, which further enable others, and so on.

A stack tree shows the relevant “slice” of the full dependency graph, going only a few nodes out. It looks something like this:

How to model a stack tree

Step 1: Determine the core tech. The first step is to decide what the actual technology in focus is. A technology in this case is a physical tool, process or system of other technologies that combine to do a job. It does not include businesses or social innovations. (A “business” is just a group of physical and social technologies united under a strategy — but we’re only concerned with the physical part here.[1])

Examples can range from the simple: hammers, glass, newspapers, or an assembly line process; to the more complex: CPUs, streaming video services, blockchains, smartphones, or nuclear reactors.

Step 2: Layers below. What are the primary technologies and processes needed to create and maintain the core tech? What does it need not only to function but to be sustainable? Clues can be found in:

  • Why now: What enabled the tech in the first place? Why wasn’t it widely used 20 or 50 years earlier?
  • Suppliers & major cost centers of businesses producing the tech. (Infrastructure, manufacturing tech, service networks…)
  • Supply chain & logistics: What gets the product/service to customers? (Transportation, shipping, internet…)
  • Distribution tech: What gets the customers to the product? (Retailers, advertising, search engines…)

Step 3: Layers above. What does the tech directly enable? It’s possible there are no layers here. Many well-known innovations don’t directly enable other tech, like Netflix.

  • What do other businesses use it for? Who is it a supplier to?
  • Is there anything in-between the technology and the ultimate end-user?
  • Is it a marketplace or multi-sided network that connects many groups of users?

Stack tree examples

Here’s a few examples of stack trees from the tech industry, although they can be drawn out for products any industry:

(The Amazon “Vampire Squid” is the best example I can think of traversing the stack, starting as an online marketplace and expanding outward in all directions: up, down, and sideways (I left out Prime, Music, Video, etc.).

What insights can be gained?

Companies are embedded in value networks because their products generally are embedded, or nested hierarchically, as components within other products and eventually within end systems of use. — Clayton Christensen

A tech stack tree is one way of looking at a company’s value network. This can lead to insights into where value flows, who captures it, and potential opportunities to expand the business.

What layers in the stack capture the most value?

Which technologies accrue most of the value depend on many things: how much value (productivity) created relative to alternatives, availability of potential substitutes, the size of the overall need, or other competitive advantages inherent to the business model.

One of the models Clayton Christensen uses makes sense to apply here: Where is the bottleneck in demand? In other words, where in the stack is the biggest difference between performance demanded and performance supplied? What needs to be better?

Nvidia is a good example here. They keep churning out GPUs with higher capabilities and the market keeps needing more. Supply hasn’t kept up with demand and that’s likely to continue for some time. This bottleneck (along with other factors ) allows the GPU layer to capture a lot of value.

Are there opportunities to expand into adjacent technologies?

Amazon (see stack above) is the prototypical example here. They started as an online marketplace with some fulfillment operations, and over time have expanded in all directions.

In more traditional business thinking, you consider expanding vertically into suppliers and customers or horizontally across industries. Traversing a tech stack is similar, but to me more focused on the true technological and needs-based relationships. Traditional business thinking would have never led to Amazon expanding into internet infrastructure via AWS.

Of course, expanding for the sake of it is never a good strategy. You have to ask:

  • Do our current products or processes give us an advantage here?
  • How much value does the layer capture? (Is it a bottleneck in demand?)
  • Are there existing barriers to entry, and if so, does our position in other stack layers help overcome them?
  • Does this improve the outcomes for our current customers?
  • Will expansion endanger our relationships with partners or customers?

Short case study: Food delivery apps

The core tech here is a mobile or desktop app where you can order food from many local restaurants and get it delivered within ~1 hour. DoorDash, UberEats, Postmates, etc.

Layers below: What are their major cost centers? Restaurants and delivery drivers. What enabled delivery apps? Primarily ubiquitous smartphones and access to GPS-based navigation. Restaurants also need to have some way to communicate, whether by phone or Wifi-based tablets, and be able to package food in proper take-out containers (plus potentially many others to manage operations).

Layers above: What needs delivery apps to run? Cloud kitchens, which operate large strategically located kitchens that can make food for many different branded “restaurants”. Recently a further layer was added with the concept of pop-up branded chains, which uses the cloud kitchen & delivery infrastructure.

What captures the value? In the stack above, smartphones capture far more value than any other tech — but they’re a platform with thousands of other use cases. In this case we just want to focus on value flow within the food delivery market. It may not be clear at first who captures more value: the delivery apps or the restaurants, given companies like DoorDash are losing so much money. But it’s clear that restaurants are not a bottleneck in demand — so it’s likely the apps that capture more value. And it seems their unit economics bear this out.

Opportunities for expansion? The clearest opportunity to expand within the tech stack is into cloud kitchens. This could potentially alienate some restaurant partners, but restaurants are so fragmented it shouldn’t matter. I think this has a lot of potential given: captive customers, synergies with delivery app, and lower costs with economies of scale and not having to operate normal restaurant ops.

Functions in the stack

How would you classify technologies in the stack? I think it’s most informative to categorize by what pattern (or archetype) they match in the greater ecosystem. These are functions that can exist in any industry or stack tree: Platforms, protocols, etc.

I’ll follow up with another post including examples of different tech functions and stack patterns.

To be continued…

Thanks to Leo Polovets and Jake Singer for helpful feedback on this post. Header photo from veeterzy on Unsplash.

Footnotes

  1. Physical technologies are “methods and designs for transforming matter, energy, and information from one state into another in pursuit of [goals]“. There are also social technologies (organizational processes, culture, values, incentive systems, memes, etc.) that evolve and build off of each other over time. (Definitions from The Origin of Wealth, by Eric Beinhocker.) ↩︎

Product Study: iPhone

One of a handful of product case studies I wrote last year to help understand successful product launches.

Apple’s iPhone was announced December 9, 2007 and released June 29, 2007. It was $499 for the 4GB version, $599 for 8GB. After 8 years it had captured 50% of U.S. smartphone market and >66% of sales, with 100 million users.

(1) Value created — Simply describe the innovation. How did it create value?

The iPhone is a pocket computer. It has typical phone capabilities including phone calls and text messaging, along with cellular internet connectivity. Differences between other smartphones at the time were:

  • Large multi-touch screen with no tactile keyboard, no need for stylus — this allowed full use of screen when not using keyboard
  • Ability to browse normal, non WAP, websites (can zoom easily using multi-touch)
  • Ability to run desktop-class applications
  • Multiple sensor inputs — proximity, light, accelerometer

(2) Value captured — Competitive advantages, barriers to entry. Why didn’t incumbents have a reason to fight them?

  • Distribution:
    • Extension from existing Apple network — iTunes, Mac OS, iPod.
    • Brand attachment to Apple.
    • Economies of scale exist with integration and complexity of engineering.
  • Switching costs once owning an iPhone.
  • Strong habit attached to usage many times / day — strong attachment to UX.
  • Phone makers saw it as toy for rich people at first. Computer makers didn’t see it as a computer (low-end disruption).

Continue reading “Product Study: iPhone” →

The dawn of immersive storytelling

From a previous #tweetstorm:

Immersive storytelling will be a big industry in the near future: movies viewed on Oculus Rift, dome-like cinemas, or interactive games. We have co’s like Jaunt, Condition One & (consumer) making 360 cameras that will be used for filming.

A new visual “grammar” will have to be discovered by filmmakers through trial and error (i.e. no fast cuts, super close-ups, etc.). Parts of the legacy film industry will rebel at first, as they have over the last 100 years since storytelling evolved from live performances to filmed, pre-recorded stories.

Just like audiences were frightened at the sight of a train barreling towards them in early theaters, there will be a learning curve for immersive experiences. Early players of demo games for the Oculus Rift have been scared to the point of ripping their headsets off. Dome cinemas could be the social alternative to VR headsets. (If you ever been on Disney’s Soarin’ Over California ride that’s an example.)

Technology-wise, I feel a complete 360 field-of-view (FOV) like this Jaunt setup won’t be the way to go. There has to be some direction to the audience’s attention. A complete FOV is too immersive and incompatible with users’ prior experiences. Maybe at some point down the road. Something like a 180-220 degree FOV + 180 up and down to allow some freedom of motion (immersion) but still directed view with surround sound.

There is lots of experimentation ahead in the near future in both technology and storytelling grammar. I look forward to both observing and participating.

The Progression of Innovation

It’s good for any investor or business person to know where their company fits when it comes to the progression of innovation. Even if a certain company or product isn’t new, at some point in time the business it’s in was. Throughout history, innovations (whether they be technological inventions or innovations in business model) came about that performed a certain “job” better than the status quo. Most of these innovations didn’t arrive spontaneously — they were built upon or evolved from their predecessors.

The following is a simplified chart/timeline of innovations in the computer industry:

Consumers purchase computer systems, with new innovations or shifts in one component (processors or operating systems) driving innovation in computer design and vice versa. Other components like storage and display also drove innovation but were less important in this context. Most of the above innovations are technical, with the exception of the commodity PC makers (Dell, Compaq, etc.) which were an innovation in business model.

After money was transferred from consumers to computer makers, it went primarily to chip makers and OS developers. Because suppliers like Intel and Microsoft had strong competitive advantages, they had strong bargaining power, and therefore received and kept most of the value.

Retail Industry

The progression of innovation doesn’t just apply to industries as technical and complex as computers. Below is another timeline (dates are approximate) of the progression of the retail industry: Continue reading “The Progression of Innovation” →

Why Google Continues to be the Best

GoogleAs many have already seen, Google just posted some great third quarter figures. Both revenue and operating income were each up 23%, and Traffic Acquisition Costs (the revenue paid to AdSense partners) were at an all-time low of 25.7% of ad revenue. They also broke out some never-before-released sales figures: $2.5 billion a year for non-text display ads, and $1 billion for Google’s mobile search (driven mostly by use of their Android OS). But one part of the conference call caught my attention:

This is why we’re incredibly proud of Google Instant. Many of you guys speculated that we launched Instant to make more money. Well, let me tell you, that’s simply not the case. We launched Instant because it’s so much better for the user. In fact, from a revenue standpoint, its impact has been very minimal. And from a resource standpoint, it’s actually pretty expensive. So why did we do it? Well, we believe from a user standpoint, Instant is outstanding—and the data that we’re seeing actually bears this out.

The above was from Jonathan Rosenberg, Google’s SVP of Product Management. So, Google Instant was an expensive, non-revenue-producing upgrade to their lucrative search product. They did it, said Rosenberg, because it’s a huge improvement to the user experience. But how can that be measured? This got me thinking about what kind of metrics are truly important to Google in a broader economic sense. In Google’s financial reports they tout improvement in metrics like Traffic Acquisition Costs, Cost-Per-Click, and total number of Paid Clicks. All important to their business, but none that really capture Google’s overall business model. The most important metric to Google, I believe, is Revenue per Unit of User’s Time (or RUUT, for short).

Translating Time into Profit

Time is the ultimate scarce resource. Most businesses capture a portion of their customer’s wallets in exchange for a good or service. But businesses like Google (and TV networks, and most new media/web-based companies) capture a portion of customer’s time first, then translate that time into revenue.

Because time is scarce, when consumers choose to devote their time to a product or service, they are doing it at the exclusion of something else. So that company is literally capturing their customer’s time.  Before Google and other search engines, when people wanted to “find” something, they went about it a multitude of ways: white & yellow pages, classifieds, a library or bookstore, or just plain leaving your house and searching (hard to believe, I know). These things took up a lot of people’s time. Continue reading “Why Google Continues to be the Best” →